基于微粒群优化的复杂环境多机器人气味源定位
|
摘要
在自然界中，许多生物利用气味信息发现同伴、搜寻食物源、进行信息交流等。随着传感器技术、机器人学和仿生学等的发展，上世纪90年代开始一些学者尝试利用“嗅觉”机器人完成气味地图构建、气味源定位和气味源分类等任务，称为机器人主动嗅觉问题，它可广泛应用于有害/有毒气体检测、灾后搜索和营救、反恐排爆等场合。
本文针对机器人通信距离受限、传感器数据含有噪声、多气味源和动态气味源等多种复杂环境，研究基于改进微粒群优化的多机器人气味源跟踪和定位方法。
首先，研究机器人通信距离受限下的气味源定位问题，提出一种动态拓扑微粒群搜索方法，用于跟踪烟羽。该方法将机器人抽象为微粒，采用Firas斥力场函数，引导机器人快速搜索烟羽；基于无线信号对数距离损耗模型，估计机器人间的通信范围，据此形成微粒群的动态拓扑结构，并确定微粒的全局极值；此外，考虑传感器采样时间和恢复时间及机器人运动速度等约束，利用改进微粒群算法实现多机器人协同气味源定位。
其次，研究传感器采样数据含有噪声的气味源定位问题，提出一种基于骨干微粒群优化的多机器人协调搜索方法。该方法将每个机器人看作一个微粒，机器人传感器探测到的气味浓度值作为微粒的适应值，所有机器人组成一个进化微粒群；采用动态统计方法在线估计机器人所测气味浓度的噪声强度，并通过区间数表示噪声环境下微粒的适应值；定义区间数的概率支配关系，给出具有区间适应值的微粒优劣比较，更新微粒个体极值和全局极值；利用关于个体极值和全局极值的高斯采样更新机器人的位置，完成气味源跟踪和定位。
然后，研究多气味源定位问题，提出一种基于小生境微粒群优化的多机器人气味源定位方法。该方法将每个机器人看作一个微粒，发现烟羽微粒与邻域微粒形成小生境，不同小生境同时定位不同气味源；考虑传感器采样/恢复时间及机器人运动速度等约束，利用改进微粒更新公式完成小生境的进化；基于类聚集度给出小生境规模的动态调整策略，使得微粒群定位尽可能多的气味源；根据不同小生境微粒全局极值间的距离及其飞行方向的相似性，定义类间合并策略，防止微粒重复搜索同一区域；最后，根据机器人所测气味浓度值和位置变化情况，确定气味源的位置。
再次，研究风速变化环境下的气味源定位问题，提出一种基于支持向量回归和微粒群优化的多机器人气味源定位方法。该方法以当前时刻机器人位置为输入，以机器人传感器所测气味浓度值为输出，组成采样样本；利用支持向量回归，建立机器人所在位置气味浓度的预测模型；采用改进微粒群优化方法定位气味源时，以气味浓度最大的机器人所在的观测窗内，基于预测模型得到的气味浓度最大值所在的位置，作为微粒的全局极值，以当前时刻机器人的位置，作为微粒的个体极值，完成微粒的更新；最后，根据机器人所测气味浓度值和位置变化情况，确定气味源的位置。
最后，研究气味源定位成功后机器人规避危险气味源的全局路径规划问题，提出一种含有自适应变异算子和非可行储备集保存策略的改进多目标微粒群优化算法。该算法采用纵横直线族的方法对环境地图进行建模，计算路径长度和危险度两个性能指标，建立路径规划问题的数学模型；为改善新生微粒(路径)的可行性，提出一种基于路径受阻程度的自适应变异算子；为提高算法的全局探索能力，除可行储备集外，采用一个非可行储备集保存迭代中所得非可行解，并从中选择微粒的全局极值；给出一种基于路径受阻程度的含约束Pareto支配关系，用来更新微粒个体极值及算法的外部储备集。
将所提方法应用于不同场景的多机器人气味源定位和包含待规避危险源的全局路径规划问题，与多种典型方法相比较，仿真实验结果验证了所提方法的可行性和有效性。
In nature, odor information is widely used by various creatures to seek for mates,foods, or exchange information. With the development of sensor techniques, robotics,and bionics, researchers have attempted to use robots with olfaction to build the mapof odor source, localize odor source and distinguish different odor sources since the1990s, which is called active robot olfaction. Part representative applications includeharmful or toxic gas detection, rescue after disasters, explosives or narcoticslocalization, and so on.
Considering the complexity of actual environments, this dissertation investigatestracing and localization of odor sources based on modified particle swarmoptimization for different plume environments, including the constraint of limitedcommunication among robots, the noise odor concentration detected by sensors,synchronously localization of multiple odor sources and timely tracing of odorsources in dynamic environments with changing wind.
Firstly, considering the constraint of limited communication among robots, amethod of localizing odor sources using multiple robots based on particle swarmoptimization is presented on the condition of abstracting each robot as a particle. Inthis method, a strategy incorporating with a repulsive function is utilized to guide arobot to rapidly search for a plume. Then the range of communication among robots isestimated based on the log-distance loss model of wireless signal propagation to forma dynamic topology structure of a particle swarm and to determine the globaloptimum of particles. Finally, the sampling/recovery time of a sensor is incorporatedto update a particle so as to trace the plume.
Secondly, the problem of odor source localization in noise environment isfocused on, and a cooperative search method of multi-robot based on particle swarmoptimization is presented. In this method, a robot is defined as a particle, odorconcentration detected by sensors of this robot is regarded as the fitness of thisparticle, and all robots form the swarm of PSO. By estimating the noise degree ofodor concentration detected by sensors using a dynamical statistic method, animproved bare-bones PSO with interval fitness is proposed to lead the particles searchcooperatively odor source. Then a probability domination relationship suitable tointerval fitness is defined to compare particles and update the local and globaloptimum of particles. Moreover, a Gauss sampling method based on the local andglobal optimum of particles is used to update the positions of particles to localize odor sources.
Thirdly, aiming at the problem of multiple odor sources localization, amulti-robot cooperation method based on niching particle swarm optimization isproposed. In this method, a robot capturing the plume and its neighbour form a niche,and different niches are employed to localize different odor sources synchronously.The position of a particle in a niche is updated using an improved PSO consideringthe constraints of the sampling/recovery time of a sensor and the velocity limit of arobot. In order to localize more odor sources, the size of each niche is dynamicallyadjusted based on the aggregation degree of its elements. Based on the similarity ofoptimal particles found by niches, a niche merging strategy is proposed to preventparticles repeatedly searching for the same region. Finally, the position of an odorsource is localized based on the concentration value and the position of a robot.
Fourthly, aiming at the problem of odor source localization in dynamicenvironments with changing wind, a method of localizing odor source using multiplerobots based on particle swarm optimization and support vector regression is proposed.In this method, a model predicting concentration of an odor at a location based onsupport vector regression is developed, which takes a robot’s current position as itsinput, and the corresponding concentration value measured by the robot as its output.Then, an improved particle swarm optimization is used to localize odor source, andthe position corresponding to the maximal concentration value obtained by theprediction model is taken as the particle’s global optimum in the observation windowof the robot with the maximal concentration value. In addition, the current position ofa robot is taken as the particle’s local optimum. The velocity and position of a particleis updated based on the above global and local optima. Finally, the position of an odorsource is localized based on the concentration value and the position of a robot.
Finally, a global robot path planning approach to evade localized danger odorsources based on multi-objective particle swarm optimization is presented in thispaper. In this method, based on the environment map of a mobile robot described witha series of horizontal and vertical lines, an optimization model of the above problemincluding two indices, i.e. the length and the danger degree of a path, is established.Then, an improved multi-objective particle swarm optimization algorithm withself-adaptive mutation operation based on the degree of a path blocked by obstaclesand an infeasible solutions archive is developed to improve the diversity andfeasibility of a new path. Moreover, a constrained Pareto domination based on thedegree of a path blocked by obstacles is employed to update local leaders of a particle and the feasible and infeasible archives.
The proposed methods are applied to odor sources localization using multiplerobots and a global path planning containing danger odor sources to be evaded invarious scenarios and the simulation experimental results confirm its feasibility andefficiency.
本文针对机器人通信距离受限、传感器数据含有噪声、多气味源和动态气味源等多种复杂环境，研究基于改进微粒群优化的多机器人气味源跟踪和定位方法。
首先，研究机器人通信距离受限下的气味源定位问题，提出一种动态拓扑微粒群搜索方法，用于跟踪烟羽。该方法将机器人抽象为微粒，采用Firas斥力场函数，引导机器人快速搜索烟羽；基于无线信号对数距离损耗模型，估计机器人间的通信范围，据此形成微粒群的动态拓扑结构，并确定微粒的全局极值；此外，考虑传感器采样时间和恢复时间及机器人运动速度等约束，利用改进微粒群算法实现多机器人协同气味源定位。
其次，研究传感器采样数据含有噪声的气味源定位问题，提出一种基于骨干微粒群优化的多机器人协调搜索方法。该方法将每个机器人看作一个微粒，机器人传感器探测到的气味浓度值作为微粒的适应值，所有机器人组成一个进化微粒群；采用动态统计方法在线估计机器人所测气味浓度的噪声强度，并通过区间数表示噪声环境下微粒的适应值；定义区间数的概率支配关系，给出具有区间适应值的微粒优劣比较，更新微粒个体极值和全局极值；利用关于个体极值和全局极值的高斯采样更新机器人的位置，完成气味源跟踪和定位。
然后，研究多气味源定位问题，提出一种基于小生境微粒群优化的多机器人气味源定位方法。该方法将每个机器人看作一个微粒，发现烟羽微粒与邻域微粒形成小生境，不同小生境同时定位不同气味源；考虑传感器采样/恢复时间及机器人运动速度等约束，利用改进微粒更新公式完成小生境的进化；基于类聚集度给出小生境规模的动态调整策略，使得微粒群定位尽可能多的气味源；根据不同小生境微粒全局极值间的距离及其飞行方向的相似性，定义类间合并策略，防止微粒重复搜索同一区域；最后，根据机器人所测气味浓度值和位置变化情况，确定气味源的位置。
再次，研究风速变化环境下的气味源定位问题，提出一种基于支持向量回归和微粒群优化的多机器人气味源定位方法。该方法以当前时刻机器人位置为输入，以机器人传感器所测气味浓度值为输出，组成采样样本；利用支持向量回归，建立机器人所在位置气味浓度的预测模型；采用改进微粒群优化方法定位气味源时，以气味浓度最大的机器人所在的观测窗内，基于预测模型得到的气味浓度最大值所在的位置，作为微粒的全局极值，以当前时刻机器人的位置，作为微粒的个体极值，完成微粒的更新；最后，根据机器人所测气味浓度值和位置变化情况，确定气味源的位置。
最后，研究气味源定位成功后机器人规避危险气味源的全局路径规划问题，提出一种含有自适应变异算子和非可行储备集保存策略的改进多目标微粒群优化算法。该算法采用纵横直线族的方法对环境地图进行建模，计算路径长度和危险度两个性能指标，建立路径规划问题的数学模型；为改善新生微粒(路径)的可行性，提出一种基于路径受阻程度的自适应变异算子；为提高算法的全局探索能力，除可行储备集外，采用一个非可行储备集保存迭代中所得非可行解，并从中选择微粒的全局极值；给出一种基于路径受阻程度的含约束Pareto支配关系，用来更新微粒个体极值及算法的外部储备集。
将所提方法应用于不同场景的多机器人气味源定位和包含待规避危险源的全局路径规划问题，与多种典型方法相比较，仿真实验结果验证了所提方法的可行性和有效性。
In nature, odor information is widely used by various creatures to seek for mates,foods, or exchange information. With the development of sensor techniques, robotics,and bionics, researchers have attempted to use robots with olfaction to build the mapof odor source, localize odor source and distinguish different odor sources since the1990s, which is called active robot olfaction. Part representative applications includeharmful or toxic gas detection, rescue after disasters, explosives or narcoticslocalization, and so on.
Considering the complexity of actual environments, this dissertation investigatestracing and localization of odor sources based on modified particle swarmoptimization for different plume environments, including the constraint of limitedcommunication among robots, the noise odor concentration detected by sensors,synchronously localization of multiple odor sources and timely tracing of odorsources in dynamic environments with changing wind.
Firstly, considering the constraint of limited communication among robots, amethod of localizing odor sources using multiple robots based on particle swarmoptimization is presented on the condition of abstracting each robot as a particle. Inthis method, a strategy incorporating with a repulsive function is utilized to guide arobot to rapidly search for a plume. Then the range of communication among robots isestimated based on the log-distance loss model of wireless signal propagation to forma dynamic topology structure of a particle swarm and to determine the globaloptimum of particles. Finally, the sampling/recovery time of a sensor is incorporatedto update a particle so as to trace the plume.
Secondly, the problem of odor source localization in noise environment isfocused on, and a cooperative search method of multi-robot based on particle swarmoptimization is presented. In this method, a robot is defined as a particle, odorconcentration detected by sensors of this robot is regarded as the fitness of thisparticle, and all robots form the swarm of PSO. By estimating the noise degree ofodor concentration detected by sensors using a dynamical statistic method, animproved bare-bones PSO with interval fitness is proposed to lead the particles searchcooperatively odor source. Then a probability domination relationship suitable tointerval fitness is defined to compare particles and update the local and globaloptimum of particles. Moreover, a Gauss sampling method based on the local andglobal optimum of particles is used to update the positions of particles to localize odor sources.
Thirdly, aiming at the problem of multiple odor sources localization, amulti-robot cooperation method based on niching particle swarm optimization isproposed. In this method, a robot capturing the plume and its neighbour form a niche,and different niches are employed to localize different odor sources synchronously.The position of a particle in a niche is updated using an improved PSO consideringthe constraints of the sampling/recovery time of a sensor and the velocity limit of arobot. In order to localize more odor sources, the size of each niche is dynamicallyadjusted based on the aggregation degree of its elements. Based on the similarity ofoptimal particles found by niches, a niche merging strategy is proposed to preventparticles repeatedly searching for the same region. Finally, the position of an odorsource is localized based on the concentration value and the position of a robot.
Fourthly, aiming at the problem of odor source localization in dynamicenvironments with changing wind, a method of localizing odor source using multiplerobots based on particle swarm optimization and support vector regression is proposed.In this method, a model predicting concentration of an odor at a location based onsupport vector regression is developed, which takes a robot’s current position as itsinput, and the corresponding concentration value measured by the robot as its output.Then, an improved particle swarm optimization is used to localize odor source, andthe position corresponding to the maximal concentration value obtained by theprediction model is taken as the particle’s global optimum in the observation windowof the robot with the maximal concentration value. In addition, the current position ofa robot is taken as the particle’s local optimum. The velocity and position of a particleis updated based on the above global and local optima. Finally, the position of an odorsource is localized based on the concentration value and the position of a robot.
Finally, a global robot path planning approach to evade localized danger odorsources based on multi-objective particle swarm optimization is presented in thispaper. In this method, based on the environment map of a mobile robot described witha series of horizontal and vertical lines, an optimization model of the above problemincluding two indices, i.e. the length and the danger degree of a path, is established.Then, an improved multi-objective particle swarm optimization algorithm withself-adaptive mutation operation based on the degree of a path blocked by obstaclesand an infeasible solutions archive is developed to improve the diversity andfeasibility of a new path. Moreover, a constrained Pareto domination based on thedegree of a path blocked by obstacles is employed to update local leaders of a particle and the feasible and infeasible archives.
The proposed methods are applied to odor sources localization using multiplerobots and a global path planning containing danger odor sources to be evaded invarious scenarios and the simulation experimental results confirm its feasibility andefficiency.
引文
[1] Mafra-Neto A, Cardé R. Fine-scale structure of pheromone plumes modulates upwindorientation of flying moths[J]. Nature,1994,369:142-144.
[2] Pyk P, Badia S B, Bernardet U, et al. An artificial moth: Chemical source localization using arobot based neuronal model of moth optomotor anemotactic search[J]. Autonomous Robots,2006,20(3):197-213.
[3] Kuwana Y, Shimoyana I. Pheromone-guided mobile robot that behaves like a silkworm mothwith living antennae as pheromone sensors[J]. The International Journal of Robotics Research,1998,17(9):924-933.
[4] Basil J, Atema J. Lobster orientation in turbulent odor plumes: simultaneous measurement oftracking behavior and temporal odor patterns[J]. The Biological Bulletin,1994,187:272-273.
[5] Grasso F W, Consi T R, Mountain D C,et al. Biomimetic robot lobster performschemo-orientation in turbulence using a pair of spatially separated sensors: progress andchallenges[J]. Robotics and Autonomous Systems,2000,30(1-2):115-131.
[6] Keller T, Powell I, Weissburg M. Role of olfactory appendages in chemically mediatedorientation of blue crabs[J]. Marine Ecology Progress Series,2003,261:217-231.
[7] Holland O, Melhuish C. Some adaptive movements of animates with single symmetricalsensors[C]. Proceedings of the4th International Conference on Simulation of AdaptiveBehaviour from animal to animats, USA:Cambridge,1996,(4):55-64.
[8] Kowadlo G, Russell R A. Robot odor localization: a taxonomy and survey[J].The InternationalJournal of Robotics Research,2008,27(8):869-894.
[9] Lilienthal A, Loutfi A, Duckett T. Airborne chemical sensing with mobile robots[J].Sensors,2006,6(11):1616-1678.
[10] Michaelides M P, Panayiotou C G. Plume source position estimation using sensor networks[C].Proceedings of the13th Mediterranean Conference on Control and Automation, USA:Limassol,2005:731-736.
[11] Langer K. A guide to sensor design for land mine detection[C]. Proceedings of the EURELInternational Conference on the Detection of Abandoned Landmines: A HumanitarianImperative Seeking a Technical Solution, USA: Edinburgh,1996:30-32.
[12] Hayes A T, Martinoli A, Goodman R M. Swarm robotic odor localization[C]. Proceedings of2001IEEE/RSJ International Conference on Intelligent Robots and Systems, Hawaii:USA,2001:1073-1078.
[13] Li J G, Meng Q H, Wang Y, et al. Odor source localization using a mobile robot in outdoorairflow environments with a particle filter algorithm[J].Autonomous Robots,2011,30(3):281-292.
[14] Ferri G, Jakuba M V, Mondini A, et al. Mapping multiple gas/odor sources in an uncontrolledindoor environment using a Bayesian occupancy grid mapping based method[J]. Robotics andAutonomous Systems,2011,59(11):988-1000.
[15] Kowadlo G, Russell R A. Improving the robustness of na ve physics airflow mapping usingBayesian reasoning on a multiple hypothesis tree[J]. Robotics and Autonomous Systems,2009,57(6-7):723-737.
[16] Pang S, Farrell J A. Chemical plume source localization[J]. IEEE Transactions on Systems,Man and Cybernetics, Part B (Cybernetics),2006,36(5):1068-1080.
[17] Lu Q, Han Q L. Decision-making in a multi-robot system for odor source localization[C].Proceedings of the37th Annual Conference of the IEEE Industrial Electronics Society,Australia: Melbourne,2011:74-79.
[18] Moraud E M, Martinez D. Effectiveness and robustness of robot infotaxis for searching indilute conditions[J]. Front Neurorobotics,2010,4(1):1-8.
[19] Zarzhitsky D, Spears D F. Swarm approach to chemical source localization[C]. Proceedings ofIEEE International Conference on Systems, Man and Cybernetics, USA: Waikoloa,2005,1435-1440.
[20] Marques L, Nunes U, Almeida A T. Olfaction-based mobile robot navigation[J]. Thin SolidFilms,2002,418(1):51-58.
[21] Lilienthal A, Duckett T. Experimental analysis of smelling braitenbergvehicles[C].Proceedings of IEEE International Conference on Advanced Robotics, Portugal:Coimbra,2003:29-38.
[22] Ishida H, Kagawa Y, Nakamoto T, et al. Odor-source localization in clean room byautonomous mobile sensing system[C]. Proceedings of International Conference onSolid-State Sensors and Actuators and Eurosensors IX, Sweden: Stockholm,1995:783-786.
[23] Russell R A, Thiel D, Deveza R, et al. Robotic system to locate hazardous chemicalleaks[C].Proceedings of the IEEE International Conference on Robotics and Automation,Japan: Nagoya,1995:556-561.
[24] Ishida H, Tanaka H, Taniguchi H, et al. Controlling a gas/odor plume-tracking robot basedon transient responses of gas sensors[J]. IEEE Sensors Journal,2005,5(3):537-545.
[25] Hayes A T, Martinoli A. Goodman R M. Distributed odor source localization[J]. IEEE SensorsJournal,2002,2(3):260-271.
[26] Lu T F. Indoor odour source localization using robot: Initial location and surge distancematter?[J]. Robotics and Autonomous Systems,2013,61(6):637-647.
[27] Lochmatter T, Martinoli A. Theoretical analysis of three Bio-inspired plume trackingalgorithms[C]. Proceedings of2009IEEE International Conference on Robotics andAutomation, Japan: Kobe,2009:2662-2668.
[28] Lochmatter T, Raemy X, Matthey L, et al. A comparison of casting and spiraling algorithmsfor odor source localization in laminar flow[C]. Proceedings of2008IEEE InternationalConference on Robotics and Automation, USA: Pasadena,2008:1138-1143.
[29] Russell R A, Bab Hadiashar A, Shepherd R L, et al. A comparison of reactive robotchemotaxis algorithms[J].Robotics and Autonomous Systems,2003,45(2):83-97.
[30] Ando N, Emoto S, Kanzaki R. Odour-tracking capability of a silkmoth driving a mobile robotwith turning bias and time delay[J]. Bioinspiration&Biomimetics,2013,8(1):1-14.
[31] Kang X D, Li W. Moth-inspired plume tracing via multiple autonomous vehicles underformation control[J]. Adaptive Behavior,2012,20(2):131-142.
[32] Marques L, Nunes U, Almeida A T. Cooperative odour field exploration with geneticalgorithms[C]. Proceedings of the5th Portuguese Conference on Automatic Control, Portugal:Averio,2002:138-143.
[33] Shi Z B, Sun J B. Mobile robots odor localization with a Genetic Algorithm[C]. Proceedingsof the4th International Conference on Computational Intelligence and CommunicationNetworks, India:Mathura,2012:875-878.
[34]骆德汉,邹宇华,庄家俊.基于修正蚁群算法的多机器人气味源定位策略研究[J].机器人,2008,30(6):536-541.
[35]孟庆浩,李飞,张明路,等.湍流烟羽环境下多机器人主动嗅觉实现方法研究[J].自动化学报,2008,34(10):1281-1290.
[36] Meng Q H, Yang W X, Wang Y, et al. Adapting an Ant Colony metaphor for multi-robotchemical plume tracing[J]. Sensors,2012,12:4737-4763.
[37] Jatmiko W, Sekiyama K, Fukuda T. A PSO based mobile sensor network for odor sourcelocalization in dynamic environment: theory, simulation and measurement[C].Proceedings ofIEEE Congress on Evolutionary Computation, Canada: Vancouver,2006:1036-1043.
[38] Jatmiko W, Sekiyama K, Fukuda T. A PSO-based mobile robot for odor source localization indynamic advection-diffusion with obstacle environment: theory, simulation andmeasurement[J]. IEEE Computational Intelligence Magazine,2007,2(2):37-51.
[39] Jatmiko W, Pambuko W, Mursanto P, et al. Localizing multiple odor sources in dynamicenvironment using ranged subgroup PSO with flow of wind based on open dynamic enginelibrary[C].Proceedings of International Symposium on Micro-NanoMechatronics and HumanScience, Japan: Nagoya,2009:602-607.
[40] Jatmiko W, Jovan F, Dhiemas RYS, et al. Robots implementation for odor source localizationusing PSO algorithm[J]. Wseas Transactions on Circuits and Systems,2011,10(4):115-125.
[41]李飞,孟庆浩,李吉功,等.基于P-PSO算法的室内有障碍通风环境下的多机器人气味源搜索[J].自动化学报,2009,35(12):1573-1579.
[42] Lu Q, Liu S R, Qiu X N.A distributed architecture with two layers for odor source localizationin multi-robot systems[C]. Proceedings of IEEE Congress on Evolutionary Computation,Spain: Barcelona,2010:1-7.
[43] Lu Q, Luo P. A learning particle swarm optimization algorithm for odor source localization[J].International Journal of Automation Computing,2011,8(3):371-380.
[44] Lu Q, Liu S R, Xie X G, et al. Decision making and finite-time motion control for a group ofrobots[J]. IEEE Transactions on Cybernetics,2013,43(2):738-750.
[45] Marques L, Nunes U, Almeida A T. Particle swarm-based olfactory guidedsearch[J].Autonomous Robots,2006,20(3):277-287.
[46] Gong D W, Qi C L, Zhang Y, et al. Modified particle swarm optimization for odor sourcelocalization of multi-robot[C]. Proceedings of the2011IEEE Congress on EvolutionaryComputation, USA: New Orleans,2011:130-136.
[47] Gong D W, Zhang Y, Qi C L. Localizing odour source using multi-robot andanemotaxis-based particle swarm optimization[J]. IET Control Theory and Applications,2012,6(11):1661-1670
[48] Jatmiko W, Pambuko W, Febrian A, et al, Ranged subgroup particle swarm optimization forlocalizing multiple odor sources[J]. International Journal on Smart Sensing and IntelligentSystems,2010,3(3):411-442.
[49] Thomas J, Ghose D. Strategies for locating multiple odor sources using glowworm swarmoptimization[C]. Proceedings of the4th Indian International Conference on ArtificialIntelligence, India:Karnataka,2009:842-861.
[50] Zhang Y L, Ma X P, Miao Y Z, Localization of multiple odor sources using modifiedglowworm swarm optimization with collective robots[C]. Proceedings of the30th ChineseControl Conference, China:Yantai,2011:1899-1904.
[51]王阳,孟庆浩,李腾,等.室内通风环境下基于模拟退火算法的单机器人气味源定位[J].机器人,2013,35(3):283-291.
[52] Marques L, Nunes U, Almeida A T. Odour searching with autonomous mobile robots: anevolutionary-based approach[C]. Proceedings of11th International Conference on AdvancedRobotics, Portugal: Coimbra,2003:494-500.
[53] Kennedy J, Eberhart R C. Particle swarm optimization[C].Proceedings of IEEE InternationalConference on Neural Networks, Australia: Perth,1995:1942-1948.
[54] Zhang T, Cai J D.A novel hybrid particle swarm optimization method applied to economicdispatch[J]. International Journal of Bio-Inspired Computation,2010,2(1):9-17.
[55] Gong D W, Zhang Y, Qi C L. Environmental/economic power dispatch using a hybridmulti-objective optimization algorithm[J].International Journal of Electrical Power andEnergy Systems,2010,32(6):607-614.
[56]巩敦卫,耿娜,张勇.多地貌环境下的移动机器人路径规划研究[J].控制与决策,2012,27(5):708-712.
[57] Mahor A, Rangnekar S. Short term generation scheduling of cascaded hydro electric systemusing novel self adaptive inertia weight PSO [J]. Electrical Power&Energy Systems,2012,34(1):1-9.
[58]张勇.区间多目标优化问题的微粒群优化理论及应用[D].徐州:中国矿业大学,2009.
[59] Rout N K, Das D P, Panda G. Particle swarm optimization based active noise control algorithmwithout secondary path identification [J]. IEEE Transactions on Instrumentation andMeasurement,2012,61(2):554-563.
[60] Scheerlinck K, Vernieuwe H, De Baets B. Zadeh’s extension principle for continuousfunctions of non-interactive variables: a parallel optimization Approach [J]. IEEETransactions on Fuzzy Systems,2012,20(1):96-108.
[61]张小俊,张明路,孟庆浩,等.一种基于动物捕食行为的机器人气味源定位策略[J].机器人,2008,30(3):268-272.
[62]李俊彩,孟庆浩,梁琼.基于进化梯度搜索的机器人主动嗅觉仿真研究[J].机器人,2007,29(3):234-238.
[63] Li W, Farrell J A, Pang S, et al. Moth-inspired chemical plume tracing on an autonomousunderwater vehicle[C]. Proceedings of IEEE Transactions on Robotics,2006,22(2):292-307.
[64] He N, Yang J H. The localization method of active olfaction robot and experimental study[J].Advanced materials research,2012,516-517:1827-1830.
[65] Cui G Q, Zhang X J, Liu Y H, et al. Research on the mobile robot’s odor source Localizationby biomimetics[C]. Proceedings of the2010IEEE International Conference on Robotics andBiomimetics, China: Tianjin,2010:1160-1164.
[66] Li J G, Yang J, Cui S G, et al. Speed limitation of a mobile robot and methodology of tracingodor plume in airflow environments.[J] Procedia Engineering,2011,15:1041–1045.
[67] Ferri G, Caselli E, Mattoli V, et al. SPIRAL: a novel biologically-inspired algorithm forgas/odor source localization in an indoor environment with no strong airflow[J]. Robotics andAutonomous Systems,2009,57(4):393-402.
[68] Wang Y, Meng Q H, Zeng M. Vision-aided spiral-surge algorithm for odor source localizationin indoor natural ventilated environments[J]. Mechanical Engineering and Technology,2012,125:751-755.
[69] Calvo J M B, Badia S B, Semo H T, et al. The real-world localization and classification ofmultiple odours using a biologically based neurorobotics approach[C]. Proceedings of The2010International Joint Conference on Neural Networks, India: Bangalore,2010:1-7.
[70] Balkovsky E, Shraiman B I. Olfactory search at high Reynolds number[C]. Proceedings of theNational Academy of Sciences of United States of America,2002,99(20):12589-12593.
[71] Cabrita G, Sousa P, Marques L. Player/Stage simulation of olfactory experiments[C].Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems,Taiwan: Taipei,2010:1120-1125.
[72] Lilienthal A, Duckett T. Building gas concentration gridmaps with a mobile robot[J]. Roboticsand Autonomous Systems,2004,48(1):3-16.
[73] Lilienthal A J, Reggente M, Trincavelli M, et al. A statistical approach to gas distributionmodelling with mobile robots-the kernel DM+V algorithm[C]. Proceedings of IEEE/RSJInternational Conference on Intelligent Robots and Systems, USA:ST Louis,2009:570-576.
[74] Kowadlo G, Russell R A. Using naive physics for odor localization in a cluttered indoorenvironments[J]. Automation Robot,2006,20(3):215-230.
[75] Ishida H, Nakamoto T, Moriizumi T. Remote sensing of gas/odor source location andconcentration distribution using mobile system[J]. Sensors and Actuators B: Chemical,1998,49(1-2):52-57.
[76] Ishida H, Nakamoto T, Moriizumi T. Remote sensing and localization of gas/odor source anddistribution using mobile sensing system[C]. Proceedings of International Conference onSolid State Sensors and Actuators, USA: Chicago,1997:559-562
[77] Lilienthal A, Ulmer H, Frohlich H, et al. Gas source declaration with a mobilerobot[C].Proceedings of IEEE International Conference on Robotics and Automation, Spain:Barcelona,2004:1430-1435.
[78] Lilienthal A, Duckett T. Creating gas concentration gridmaps with a mobilerobot[C].Proceedings of the IEEE/RSJ International Conference on Intelligent Robots andSystems, USA: Las Vegas,2003:118-123.
[79] Li W. Abstraction of odor source declaration algorithm from moth-inspired plume tracingstrategies[C]. Proceedings of IEEE International Conference on Robotics and Biomimetics,China: Kunming,2006:1024-1029.
[80] Li J G, Meng Q H, Wang Y, et al. Single odor source declaration in outdoor time-variantairflow environments[C]. Proceedings of the2010IEEE International Conference on Roboticsand Biomimetics, China: Tianjin,2010:143-148.
[81] Kowadlo G，Rawlinson D， Russell R A，et al. Bi-Modal search using complementarysensing (Olfaction/Vision) for odour source localization[C]. Proceedings of IEEEInternational Conference on Robotics and Automation, USA:Florida,2006:2041-2046.
[82] Loutfi A, Coradeschi S. Smell, Think, Act: A cognitive robot discriminating odours[J].Autonomous Robot,2006,20(3):239-249.
[83] Ishida H, Yamanaka T, Kushida N, et al. Study of real-time visualization of gas/odor flowimages using gas sensor array[J]. Sensors and Actuators B,2000,65(1-3):14-16.
[84] Lilienthal A J, Ulmer H, Frohlich H, et al. Learning to detect proximity to a gas source with amobile robot[C]. Proceedings of IEEE Intelligent Robots and Systems, Japan:Sendai,2004:1444-1449.
[85] Ishida H, Wada Y, Matsukura H, Chemical sensing in robotic applications: a review[J]. IEEESensors Journal,2012,12(11):3163-3173.
[86] Zhang S Q, Xu D M. A Survey of Biologically inspired chemical plume tracking strategiesfor single robot in2-D turbulence dominated flow environments[C]. Proceedings ofIEEE/SICE International Symposium on System Integration, Japan:Kyoto,2011,348-353.
[87] Turduev M, Cabrita G, Kirtay M, et al. Experimental studies on chemical concentration mapbuilding by a multi-robot system using bio-inspired algorithms[J]. Autonomous Agents andMulti-Agent Systems,2012,1-29.
[88]李飞.湍动气流主控环境下多机器人气味源定位[D].天津:天津大学,2009.
[89]蒋萍.融合机器人视/嗅觉信息的气体泄露源定位[D].天津:天津大学,2010.
[90]李吉功.室外时变气流环境下机器人气味源定位[D].天津:天津大学,2010.
[91]路光达.基于仿生行为的移动机器人主动嗅觉技术研究[D].天津:河北工业大学,2010.
[92]韩冰.湍流环境下化学羽状物定位技术研究[D].哈尔滨:哈尔滨工程大学,2012.
[93]谭民,王硕.机器人技术研究进展[J].自动化学报,2013,39(7):963-972.
[94]倪春波,孔一斐,杨月全,等.粒子群优化及其在多机器人系统中的引用展望[J].中南大学学报(自然科学版),2013,44(s2):126-131.
[95]刘华军,杨静宇,陆建峰,等.移动机器人运动规划研究综述[J].中国工程科学，2006,8(1)：85-94.
[96] Masehian E, Sedighizadeh D. Classic and heuristic approaches in robot motion planning–achronological review[C]. Proceedings of World Academy of Science, Engineering andTechnology, India:Raygada,2007:101-106.
[97] Shi Y H, Eberhart R C. A modified particle swarm optimizer [C]. Proceedings of the IEEECongress on Evolutionary Computation, USA:Anchorage,1998:69-73.
[98] Hao Y, Zu W, Zhao Y. Real-time obstacle avoidance method based on polar coordinationparticle swarm optimization in dynamic environment[C]. Proceedings of the2nd IEEEConference on Industrial Electronics and Applications, China:Harbin,2007:1612-1617.
[99]祖伟,李刚,齐正霞.基于改进粒子群优化算法的路径规划方法研究[J].弹箭与制导学报，2008,28(4):68-70.
[100]孙波,陈卫东,席裕庚.基于粒子群优化算法的移动机器人全局路径规划[J].控制与决策,2005,20(9):1052-1060.
[101] Xue Y H, Tian G H, Huang B.Optimal robot path planning based on danger degreemap[C]. Proceedings of IEEE International Conference on Automation and Logistics,China:Shenyang,2009:1040-1045.
[102] Ma Q Z, Lei X J, Zhang Q. Mobile robot path planning with complex constraints basedon the second-order oscillating particle swarm optimization algorithm[C]. Proceedings ofWorld Congress on Computer Science and Information Engineering, USA:Los Angeles,2009:244-248.
[103]陈德宝,赵春霞.基于内分泌调节机制的粒子群算法[J].控制理论与应用,2007,24(6):1005-1009.
[104]高晓巍.基于量子行为粒子群优化算法的路径规划[J].科技通报,2013,29(7):143-146.
[105]谭冠政,刘关俊.基于粒子群算法的移动机器人全局最优路径规划[J].计算机应用研究,2007,24(11):210-212.
[106]王艳,朱庆保.基于二进制粒子群算法的移动机器人路径规划[J].南京师范大学学报(工程技术版),2009，9(2):72-78.
[107] Wei L S, Dai X F. Particle swarm optimization based on cloud theory for robot pathplanning[J]. ICIC Express Letters Part B: Applications,2013,4(3):633-638.
[108]张超,李擎,陈鹏,等.一种基于粒子群参数优化的改进蚁群算法及其应用[J].北京科技大学学报,2013,35(7):955-960.
[109] Zhang Q R, Gu G C. Path planning based on improved binary particle swarmoptimization algorithm[C]. Proceedings of the2008IEEE International Conference onRobotics, Automation and Mechatronics, China:Chendu,2008:462-466.
[110] Hu C Y, Wu X N, Liang Q Z, et al. Autonomous robot path planning based on swarmintelligence and stream functions[J]. Lecture Notes in Computer Science,2007,4684:277-284.
[111] Hereford J M, Siebold M, Nichols S. Using the particle swarm optimization algorithmfor robotic search applications[C]. Proceedings of the IEEE Swarm Intelligence Symposium,USA:Honolulu,2007:53-59.
[112] Bashyal S, Venayagamoorthy G K. Human swarm interaction for radiation sourcesearch and localization[C]. Proceedings of IEEE Swarm Intelligence Symposium,USA:St.Louis,2008:1-8.
[113] Li G S, Shi H B. Path planning for mobile robot based on particle swarmoptimization[C]. Chinese Control and Decision Conference, China:Yantai,2008:3290-3294.
[114] Kang Hwan, Lee Byunghee, Kim Kabi. Path planning algorithm using the particleswarm optimization and the improved Dijkstra algorithm[C]. Proceedings of the2008Pacific-Asia Workshop on Computational Intelligence and Industrial Application,China:Wuhan,2008:1002-1004.
[115]秦元庆,孙德宝,李宁,等.基于粒子群算法的移动机器人路径规划[J].机器人,2004,26(3):222-225.
[116]昝杰,蔡宗琰,梁虎,等.基于Bezier曲线的自主移动机器人最优路径规划[J].兰州大学学报(自然科学版),2013,49(2):249-254.
[117] Tang T. Path planning of motion robot based on particle swarm optimization and FSVM[J]. International Journal of Advancements in Computing Technology,2012,4(21):642-649.
[118] Kennedy J, Mendes R. Population structure and particle swarm performance[C].Proceedings of the IEEE International Conference on Evolutionary Computation,USA:Hawaii,2002:1671-1676.
[119] Kennedy J, Mendes R. Neighborhood topologies in fully-informed and best-of-neighborhood particle swarms[C]. Proceedings of the2003IEEE International Workshop onSoft Computing in Industrial Application, USA:New York,2003:45-50.
[120] Ghosh P, Zafar H, Das S, et al. Hierarchical dynamic neighborhood based particleswarm optimization for global optimization [C]. IEEE Congress on EvolutionaryComputation, USA:New Orleans,2011:757-764.
[121] Khatib O. Real-time obstacle avoidance for manipulators and mobilerobots[J].International Journal of Robotics Research,1986,5(1):90-98.
[122] Roads J, Escudero C J, Iglesia D I. Bayesian filtering for a bluetooth positioningsystem[C].Proceedings of IEEE International Symposium on Wireless CommunicationSystems, Iceland:Reykjavik,2008:618-622.
[123]殷剑宏,吴开亚.图论及其算法[M].合肥:中国科学技术大学出版社,2003.
[124] Lu T F. Indoor odour source localisation using robot: initial location and surge distancematter?[J]. Robotics and Autonomous Systems,2013,61(6):637-647.
[125]韩占忠,王敬,兰小平.FLUENT——流体工程仿真计算实例与应用（第2版）[M].北京:北京理工大学出版社,2010.
[126]周勇,巩敦卫.交互式遗传算法的噪声及降噪策略[J].控制理论与应用,2008,25(2):221-227.
[127] Pan H, Wang L, Liu B. Particle swarm optimization for function optimization in noisyenvironment[J]. Applied Mathematics and Computation,2006,18(12):908-919.
[128] Janson S, Middendorf M. A hierarchical particle swarm optimizer for noisy anddynamic environments[J]. Genetic Programming and Evolvable Machines,2006,7(4):329-354.
[129] Rada-Vilela J, Zhang M J, Johnston M. Resampling in particle swarm optimization[C].2013IEEE Congress on Evolutionary Computation, Mexico: Cancun,2013:947-954.
[130] Han L, He X. A novel opposition-based particle swarm optimization for noisyproblems[C]. Proceedings of the Third International Conference on Natural Computation,China: Haikou,2007:624-629.
[131] Mendel E, Krohling R A, Campos M. Swarm algorithms with chaotic jumps applied tonoisy optimization problems[J]. Information Science,2010,181(20):4494-4514.
[132]张敏慧,马龙华,郑泳凌,等.区间数不等式约束不确定系统的线性规划[J].运筹与管理,2005,14(2):32-36.
[133] Sengupta A, Pal T K, Chakraborty D. Interpretion of inequality constraints involvinginterval coefficients and a solution to interval linear programming[J]. Fuzzy Sets and Systems,2001,119(1):119-129.
[134]张勇,巩敦卫,张芹英,等.带区间约束不确定优化问题的确定化描述[J]．系统工程理论与实践,2009,29(2):127-133.
[135] Kennedy J. Bare bones particle swarms[C]. Proceedings of the2003IEEE SwarmIntelligence Symposium, USA:Indianapolis,2003:80-87.
[136] Poli R, Langdon W B. Markov chain models of Bare-bones particle swarmoptimizers[C]. Genetic and Evolutionary Computation Conference, England: London,2007:142-149.
[137] Wang P P, Shi L P, Zhang Y, et al. A hybrid simplex search and modified bare-bonesparticle swarm optimization[J]. Chinese Journal of Electronics,2013,22(1):104-108.
[138] Das S, Maity S, Qu B Y, et al. Real-parameter evolutionary multimodal optimization-asurvey of the state-of-the-art[J]. Swarm and Evolutionary Computation,2011,1(2):71-88.
[139] Parrott D, Li X D. Locating and tracking multiple dynamic optima by a particle swarmmodel using speciation [J]. IEEE Transactions on Evolutionary Computation,2006,10(4):440-458.
[140] Yang S X, Li C H. A clustering particle swarm optimizer for locating and trackingmultiple optima in dynamic environments [J]. IEEE Transactions on EvolutionaryComputation,2010,14(6):959-974.
[141] Qu B Y, Suganthan P N, Das S. A distance-based locally informed particle swarm modelfor multimodal optimization [J]. IEEE Transactions on Evolutionary Computation,2013,17(3):387-402.
[142] Li X D. Niching without niching parameters: particle swarm optimization using a ringtopology [J]. IEEE Transactions on Evolutionary Computation,2010,14(1):150-169.
[143] Afsahi Z, Meybodi M R. A new approach on particle swarm optimization formultimodal functions [C]. Proceedings of the IEEE International Conference on IntelligentComputing and Intelligent Systems, China: Shanghai,2009:303-308.
[144] Jatmiko W, Nugraha A, Effendi R, et al. Localizing multiple odor sources in a dynamicenvironment based on modified niche particle swarm optimization with flow of wind[J].Wseas Transactions on Systems,2009,11(8):1187-1196.
[145] Goldberg D E, Richardson J. Genetic algorithm with sharing for multimodal functionoptimization[C]. Proceedings of the2nd International Conference on Genetic Algorithmsand their application, USA: Hillsdale,1987:41-49.
[146] Kennedy J. Stereotyping: improving particle swarm performance with clusteranalysis[C]. Proceedings of the Congress on Evolutionary Computation, New Jersey:Piscataway,2000:1507–1512.
[147] Parsopoulos K E, Plagianakos V P, Magoulas G D, et al. Stretching technique forobtaining global minimizers through particle swarm optimization[C]. Proceedings of theParticle Swarm Optimization Workshop, USA: Indianapolis,2001:22-29.
[148] Li X. Adaptively choosing neighborhood bests using species in a particle swarmoptimizer for multimodal function optimization[J]. Lecture Notes in Computer Science,2004,3102:105-116.
[149] Brits R, Engelbrecht A P, van den Bergh F. Locating multiple optima using particleswarm optimization[J]. Applied Mathematics and Computation,2007,189(2):1859-1883.
[150] Li J P, Wood A. Random search with species conservation for multimodal functions[C].Proceedings of the Eleventh Conference on Congress on Evolutionary Computation, Norway:Trondheim,2009:3164-3171.
[151]章军.小生境粒子群优化算法及其在多分类器集成中的应用研究[D].合肥:中国科技大学,2007.
[152] Brits R, Engelbrecht A, Bergh F D. A niching particle swarm optimizer[C]. Proceedingsof the4th Asia-Pacific Conference on Simulated Evolution and Learning2002, USA: Orchild,2002:692-696.
[153] Nickabadi A, Ebadzadhe M M, Safabakhsh R. A dynamic niching particle swarmoptimizer for multi-modal optimization[C]. Proceedings of the Congress on EvolutionaryComputation, China: Hong Kong,2008:26-32.
[154] Li X. Multimodal function optimization based on fitness Euclidean-distance ratio[C].Proceedings of the Genetic and Evolutionary Computation Conference, England: London,2007:78-85.
[155] Schoeman I, Engelbrecht A P. Using vector operations to identify niches for particleswarm optimization[C]. Proceedings of IEEE Conference on Cybernetics and IntelligentSystems, Singapore,2004:361-366.
[156] Branke J. Memory enhanced evolutionary algorithms for changing optimizationproblems[C]. Proceedings of IEEE Congress on Evolutionary Computation, USA:Washington,1999:1875-1882.
[157] Hu X, Eberhart R. Adaptive particle swarm optimization: detection and response todynamic systems[C]. Proceedings of IEEE Congress on Evolutionary Computation, USA:Hawaii,2002:1666-1670.
[158] Carlisle A, Dozier G. Adapting particle swarm optimization to dynamic environments[C].Proceedings of International Conference on Artificial Intelligence, USA: Las Vegas,2000:429-434.
[159] Singh H K, Isaacs A, Nguyen T T, et al. Performance of infeasibility driven evolutionaryalgorithm (IDEA) on constrained dynamic single objective optimization problems[C].Proceedings of IEEE Congress on Evolutionary Computation, Norway:Trondheim,2009:3127-3134.
[160] Li X, Branke J, Blackwell T. Particle swarm with speciation and adaptation in a dynamicenvironment[C]. Proceedings of Genetic and Evolutionary Computation Conference, USA:New York,2006:51-58.
[161] Vavak F, Jukes K A, Fogarty T C. Performance of a genetic algorithm with variable localsearch range relative to frequency for the environmental changes[C]. Proceedings ofInternational Conference on Genetic Programming, USA:Morgan Kaufmann,1998:22-25.
[162] Janson S, Middendorf M. A hierarchical particle swarm optimizer for noisy and dynamicenvironments[J]. Genetic Programming and Evolvable Machines,2006,7(4):329-354.
[163] Yang S, Yao X. Experimental study on population-based incremental learningalgorithms for dynamic optimization problems[J]. Soft Computing A-Fusion of Foundations,Methodologies and Applications,2005,9(11):815-834.
[164] Yang S. On the design of diploid genetic algorithms for problem optimization indynamic environments[C]. Proceedings of IEEE Congress on Evolutionary Computation,Canada: Vancouver,2006:1362-1369.
[165] Daneshyari M, Yen G. Dynamic optimization using cultural based PSO[C]. Proceedingsof IEEE Congress on Evolutionary Computation, USA: New Orleans,2011:509-516.
[166] Mavrovouniotis M, Yang S. Memory-based immigrants for ant colony optimization inchanging environments[C]. Proceedings of the International Conference on Applications ofEvolutionary Computation, Germany: Berlin,2011:324-333.
[167] Simoes A, Costa E. Improving prediction in evolutionary algorithms for dynamicenvironments[C]. Proceedings of Genetic and Evolutionary Computation Conference, USA:New York,2009:875-882.
[168] Fernandez J L, Arcos J L. Adapting particle swarm optimization in dynamic and noisyenvironments[C]. Proceedings of IEEE Congress on Evolutionary Computation, Spain:Barcelona,2010:765-772.
[169]陈莉,丁立新.动态优化算法综述[J].武汉大学学报(理学版),2011,57(3):255-264.
[170]陈杰,潘峰,王光辉.粒子群优化方法在动态优化中的研究现状[J].智能系统学报,2009,4(3):189-197.
[171] Dae II J, Young-Oh K. Combining single-value streamflow forecasts-A review andguidelines for selecting techniques[J]. Journal of Hydrology,2009,37(3-4):284-299.
[172] Fu T C. A review on time series data mining[J]. Engineering Applications of ArtificialIntelligence,2011,24(1):164-181.
[173] Marvuglia A, Messineo A. Using recurrent artificial neural networks to forecasthousehold rlectricity consumption[J]. Energy Procedia,2012,(14):45-55.
[174] Arindam C, Kajal D. Fuzzy support vector machine for bankruptcy prediction[J].Applied Soft Computing,2011,11(2):2472-2486.
[175]安德洪,韩文秀,岳毅宏.组合预测法的改进及其在负荷预测中的应用[J].系统工程与电子技术,2004,26(6):842-844.
[176]张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):32-42.
[177] Shawe-Taylor J, Sun S L.A review of optimization methodologies in support vectormachines[J]. Neurocomputing,2011,74(17):3609-3618.
[178] Hsu C W, Chang C C, Lin C J. A practical guide to support vector classification[EB/OL].http://www. csie. ntu.edu. tw/~cjlin/papers/guide/guide. pdf,2003-08-10.
[179]肖建,于龙,白裔峰.支持向量回归中核函数和超参数选择方法综述[J].西南交通大学学报,2008,43(3):297-303.
[180]王书舟,伞冶.支持向量机的训练算法综述[J].智能系统学报,2008,3(6):467-475.
[181]丁世飞,齐炳娟,谭红艳.支持向量机理论与算法研究综述[J].电子科技大学学报,2011,40(1):2-10.
[182] Reyes-Sierra M, Coello Coello C A. Multi-objective particle swarm optimizers: a surveyof the state-of-the-art [J]. International Journal of Computational Intelligence Research,2006,2(3):287-308.
[183] Shi Y, Eberhart R C. Particle swarm optimization with fuzzy adaptive inertia weight[C].Proceedings of the Workshop on Particle Swarm optimization2001, USA: Indianapolis,2001:101-106.
[184] Cai Z X, Wang Y. A multi-objective optimization-based evolutionary algorithm forconstrained optimization [J]. IEEE Transactions on Evolutionary Computation,2006,10(6):658-674.
[185] Woldesenbet Y G, Tessema B G, Yen G G. Constraint handling in multi-objectiveevolutionary optimization [C]. Proceedings of the IEEE Congress on EvolutionaryComputation, Singapore,2007:3077-3084.
[186] Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-II [J]. IEEE Transactions on Evolutionary Computations,2002,6(2):182-197.
[187] Shi Y, Eberhart R C. Empirical study of particle swarm optimization [C]. Proceedings ofthe IEEE Congress on Evolutionary Computation, USA: Washington,1999:1945-1950.
[188] Zitzler E, Thiele L. Multiobjective evolutionary algorithm: a comparative case study andthe strength pareto approach [J]. IEEE Transactions on Evolutionary Computations,1999,3(4):257-271.
[189] Zitzler, E, Deb K, Thiele L. Comparison of multi-objective evolutionary algorithms:empirical results[J]. Evolutionary Computation,2000,8(2):173-195.
[190] Wolpert D H, Macready W G. No free lunch theorems for optimization[J]. IEEETransactions on Evolutionary Computation,1997,1(1):67-82.
[2] Pyk P, Badia S B, Bernardet U, et al. An artificial moth: Chemical source localization using arobot based neuronal model of moth optomotor anemotactic search[J]. Autonomous Robots,2006,20(3):197-213.
[3] Kuwana Y, Shimoyana I. Pheromone-guided mobile robot that behaves like a silkworm mothwith living antennae as pheromone sensors[J]. The International Journal of Robotics Research,1998,17(9):924-933.
[4] Basil J, Atema J. Lobster orientation in turbulent odor plumes: simultaneous measurement oftracking behavior and temporal odor patterns[J]. The Biological Bulletin,1994,187:272-273.
[5] Grasso F W, Consi T R, Mountain D C,et al. Biomimetic robot lobster performschemo-orientation in turbulence using a pair of spatially separated sensors: progress andchallenges[J]. Robotics and Autonomous Systems,2000,30(1-2):115-131.
[6] Keller T, Powell I, Weissburg M. Role of olfactory appendages in chemically mediatedorientation of blue crabs[J]. Marine Ecology Progress Series,2003,261:217-231.
[7] Holland O, Melhuish C. Some adaptive movements of animates with single symmetricalsensors[C]. Proceedings of the4th International Conference on Simulation of AdaptiveBehaviour from animal to animats, USA:Cambridge,1996,(4):55-64.
[8] Kowadlo G, Russell R A. Robot odor localization: a taxonomy and survey[J].The InternationalJournal of Robotics Research,2008,27(8):869-894.
[9] Lilienthal A, Loutfi A, Duckett T. Airborne chemical sensing with mobile robots[J].Sensors,2006,6(11):1616-1678.
[10] Michaelides M P, Panayiotou C G. Plume source position estimation using sensor networks[C].Proceedings of the13th Mediterranean Conference on Control and Automation, USA:Limassol,2005:731-736.
[11] Langer K. A guide to sensor design for land mine detection[C]. Proceedings of the EURELInternational Conference on the Detection of Abandoned Landmines: A HumanitarianImperative Seeking a Technical Solution, USA: Edinburgh,1996:30-32.
[12] Hayes A T, Martinoli A, Goodman R M. Swarm robotic odor localization[C]. Proceedings of2001IEEE/RSJ International Conference on Intelligent Robots and Systems, Hawaii:USA,2001:1073-1078.
[13] Li J G, Meng Q H, Wang Y, et al. Odor source localization using a mobile robot in outdoorairflow environments with a particle filter algorithm[J].Autonomous Robots,2011,30(3):281-292.
[14] Ferri G, Jakuba M V, Mondini A, et al. Mapping multiple gas/odor sources in an uncontrolledindoor environment using a Bayesian occupancy grid mapping based method[J]. Robotics andAutonomous Systems,2011,59(11):988-1000.
[15] Kowadlo G, Russell R A. Improving the robustness of na ve physics airflow mapping usingBayesian reasoning on a multiple hypothesis tree[J]. Robotics and Autonomous Systems,2009,57(6-7):723-737.
[16] Pang S, Farrell J A. Chemical plume source localization[J]. IEEE Transactions on Systems,Man and Cybernetics, Part B (Cybernetics),2006,36(5):1068-1080.
[17] Lu Q, Han Q L. Decision-making in a multi-robot system for odor source localization[C].Proceedings of the37th Annual Conference of the IEEE Industrial Electronics Society,Australia: Melbourne,2011:74-79.
[18] Moraud E M, Martinez D. Effectiveness and robustness of robot infotaxis for searching indilute conditions[J]. Front Neurorobotics,2010,4(1):1-8.
[19] Zarzhitsky D, Spears D F. Swarm approach to chemical source localization[C]. Proceedings ofIEEE International Conference on Systems, Man and Cybernetics, USA: Waikoloa,2005,1435-1440.
[20] Marques L, Nunes U, Almeida A T. Olfaction-based mobile robot navigation[J]. Thin SolidFilms,2002,418(1):51-58.
[21] Lilienthal A, Duckett T. Experimental analysis of smelling braitenbergvehicles[C].Proceedings of IEEE International Conference on Advanced Robotics, Portugal:Coimbra,2003:29-38.
[22] Ishida H, Kagawa Y, Nakamoto T, et al. Odor-source localization in clean room byautonomous mobile sensing system[C]. Proceedings of International Conference onSolid-State Sensors and Actuators and Eurosensors IX, Sweden: Stockholm,1995:783-786.
[23] Russell R A, Thiel D, Deveza R, et al. Robotic system to locate hazardous chemicalleaks[C].Proceedings of the IEEE International Conference on Robotics and Automation,Japan: Nagoya,1995:556-561.
[24] Ishida H, Tanaka H, Taniguchi H, et al. Controlling a gas/odor plume-tracking robot basedon transient responses of gas sensors[J]. IEEE Sensors Journal,2005,5(3):537-545.
[25] Hayes A T, Martinoli A. Goodman R M. Distributed odor source localization[J]. IEEE SensorsJournal,2002,2(3):260-271.
[26] Lu T F. Indoor odour source localization using robot: Initial location and surge distancematter?[J]. Robotics and Autonomous Systems,2013,61(6):637-647.
[27] Lochmatter T, Martinoli A. Theoretical analysis of three Bio-inspired plume trackingalgorithms[C]. Proceedings of2009IEEE International Conference on Robotics andAutomation, Japan: Kobe,2009:2662-2668.
[28] Lochmatter T, Raemy X, Matthey L, et al. A comparison of casting and spiraling algorithmsfor odor source localization in laminar flow[C]. Proceedings of2008IEEE InternationalConference on Robotics and Automation, USA: Pasadena,2008:1138-1143.
[29] Russell R A, Bab Hadiashar A, Shepherd R L, et al. A comparison of reactive robotchemotaxis algorithms[J].Robotics and Autonomous Systems,2003,45(2):83-97.
[30] Ando N, Emoto S, Kanzaki R. Odour-tracking capability of a silkmoth driving a mobile robotwith turning bias and time delay[J]. Bioinspiration&Biomimetics,2013,8(1):1-14.
[31] Kang X D, Li W. Moth-inspired plume tracing via multiple autonomous vehicles underformation control[J]. Adaptive Behavior,2012,20(2):131-142.
[32] Marques L, Nunes U, Almeida A T. Cooperative odour field exploration with geneticalgorithms[C]. Proceedings of the5th Portuguese Conference on Automatic Control, Portugal:Averio,2002:138-143.
[33] Shi Z B, Sun J B. Mobile robots odor localization with a Genetic Algorithm[C]. Proceedingsof the4th International Conference on Computational Intelligence and CommunicationNetworks, India:Mathura,2012:875-878.
[34]骆德汉,邹宇华,庄家俊.基于修正蚁群算法的多机器人气味源定位策略研究[J].机器人,2008,30(6):536-541.
[35]孟庆浩,李飞,张明路,等.湍流烟羽环境下多机器人主动嗅觉实现方法研究[J].自动化学报,2008,34(10):1281-1290.
[36] Meng Q H, Yang W X, Wang Y, et al. Adapting an Ant Colony metaphor for multi-robotchemical plume tracing[J]. Sensors,2012,12:4737-4763.
[37] Jatmiko W, Sekiyama K, Fukuda T. A PSO based mobile sensor network for odor sourcelocalization in dynamic environment: theory, simulation and measurement[C].Proceedings ofIEEE Congress on Evolutionary Computation, Canada: Vancouver,2006:1036-1043.
[38] Jatmiko W, Sekiyama K, Fukuda T. A PSO-based mobile robot for odor source localization indynamic advection-diffusion with obstacle environment: theory, simulation andmeasurement[J]. IEEE Computational Intelligence Magazine,2007,2(2):37-51.
[39] Jatmiko W, Pambuko W, Mursanto P, et al. Localizing multiple odor sources in dynamicenvironment using ranged subgroup PSO with flow of wind based on open dynamic enginelibrary[C].Proceedings of International Symposium on Micro-NanoMechatronics and HumanScience, Japan: Nagoya,2009:602-607.
[40] Jatmiko W, Jovan F, Dhiemas RYS, et al. Robots implementation for odor source localizationusing PSO algorithm[J]. Wseas Transactions on Circuits and Systems,2011,10(4):115-125.
[41]李飞,孟庆浩,李吉功,等.基于P-PSO算法的室内有障碍通风环境下的多机器人气味源搜索[J].自动化学报,2009,35(12):1573-1579.
[42] Lu Q, Liu S R, Qiu X N.A distributed architecture with two layers for odor source localizationin multi-robot systems[C]. Proceedings of IEEE Congress on Evolutionary Computation,Spain: Barcelona,2010:1-7.
[43] Lu Q, Luo P. A learning particle swarm optimization algorithm for odor source localization[J].International Journal of Automation Computing,2011,8(3):371-380.
[44] Lu Q, Liu S R, Xie X G, et al. Decision making and finite-time motion control for a group ofrobots[J]. IEEE Transactions on Cybernetics,2013,43(2):738-750.
[45] Marques L, Nunes U, Almeida A T. Particle swarm-based olfactory guidedsearch[J].Autonomous Robots,2006,20(3):277-287.
[46] Gong D W, Qi C L, Zhang Y, et al. Modified particle swarm optimization for odor sourcelocalization of multi-robot[C]. Proceedings of the2011IEEE Congress on EvolutionaryComputation, USA: New Orleans,2011:130-136.
[47] Gong D W, Zhang Y, Qi C L. Localizing odour source using multi-robot andanemotaxis-based particle swarm optimization[J]. IET Control Theory and Applications,2012,6(11):1661-1670
[48] Jatmiko W, Pambuko W, Febrian A, et al, Ranged subgroup particle swarm optimization forlocalizing multiple odor sources[J]. International Journal on Smart Sensing and IntelligentSystems,2010,3(3):411-442.
[49] Thomas J, Ghose D. Strategies for locating multiple odor sources using glowworm swarmoptimization[C]. Proceedings of the4th Indian International Conference on ArtificialIntelligence, India:Karnataka,2009:842-861.
[50] Zhang Y L, Ma X P, Miao Y Z, Localization of multiple odor sources using modifiedglowworm swarm optimization with collective robots[C]. Proceedings of the30th ChineseControl Conference, China:Yantai,2011:1899-1904.
[51]王阳,孟庆浩,李腾,等.室内通风环境下基于模拟退火算法的单机器人气味源定位[J].机器人,2013,35(3):283-291.
[52] Marques L, Nunes U, Almeida A T. Odour searching with autonomous mobile robots: anevolutionary-based approach[C]. Proceedings of11th International Conference on AdvancedRobotics, Portugal: Coimbra,2003:494-500.
[53] Kennedy J, Eberhart R C. Particle swarm optimization[C].Proceedings of IEEE InternationalConference on Neural Networks, Australia: Perth,1995:1942-1948.
[54] Zhang T, Cai J D.A novel hybrid particle swarm optimization method applied to economicdispatch[J]. International Journal of Bio-Inspired Computation,2010,2(1):9-17.
[55] Gong D W, Zhang Y, Qi C L. Environmental/economic power dispatch using a hybridmulti-objective optimization algorithm[J].International Journal of Electrical Power andEnergy Systems,2010,32(6):607-614.
[56]巩敦卫,耿娜,张勇.多地貌环境下的移动机器人路径规划研究[J].控制与决策,2012,27(5):708-712.
[57] Mahor A, Rangnekar S. Short term generation scheduling of cascaded hydro electric systemusing novel self adaptive inertia weight PSO [J]. Electrical Power&Energy Systems,2012,34(1):1-9.
[58]张勇.区间多目标优化问题的微粒群优化理论及应用[D].徐州:中国矿业大学,2009.
[59] Rout N K, Das D P, Panda G. Particle swarm optimization based active noise control algorithmwithout secondary path identification [J]. IEEE Transactions on Instrumentation andMeasurement,2012,61(2):554-563.
[60] Scheerlinck K, Vernieuwe H, De Baets B. Zadeh’s extension principle for continuousfunctions of non-interactive variables: a parallel optimization Approach [J]. IEEETransactions on Fuzzy Systems,2012,20(1):96-108.
[61]张小俊,张明路,孟庆浩,等.一种基于动物捕食行为的机器人气味源定位策略[J].机器人,2008,30(3):268-272.
[62]李俊彩,孟庆浩,梁琼.基于进化梯度搜索的机器人主动嗅觉仿真研究[J].机器人,2007,29(3):234-238.
[63] Li W, Farrell J A, Pang S, et al. Moth-inspired chemical plume tracing on an autonomousunderwater vehicle[C]. Proceedings of IEEE Transactions on Robotics,2006,22(2):292-307.
[64] He N, Yang J H. The localization method of active olfaction robot and experimental study[J].Advanced materials research,2012,516-517:1827-1830.
[65] Cui G Q, Zhang X J, Liu Y H, et al. Research on the mobile robot’s odor source Localizationby biomimetics[C]. Proceedings of the2010IEEE International Conference on Robotics andBiomimetics, China: Tianjin,2010:1160-1164.
[66] Li J G, Yang J, Cui S G, et al. Speed limitation of a mobile robot and methodology of tracingodor plume in airflow environments.[J] Procedia Engineering,2011,15:1041–1045.
[67] Ferri G, Caselli E, Mattoli V, et al. SPIRAL: a novel biologically-inspired algorithm forgas/odor source localization in an indoor environment with no strong airflow[J]. Robotics andAutonomous Systems,2009,57(4):393-402.
[68] Wang Y, Meng Q H, Zeng M. Vision-aided spiral-surge algorithm for odor source localizationin indoor natural ventilated environments[J]. Mechanical Engineering and Technology,2012,125:751-755.
[69] Calvo J M B, Badia S B, Semo H T, et al. The real-world localization and classification ofmultiple odours using a biologically based neurorobotics approach[C]. Proceedings of The2010International Joint Conference on Neural Networks, India: Bangalore,2010:1-7.
[70] Balkovsky E, Shraiman B I. Olfactory search at high Reynolds number[C]. Proceedings of theNational Academy of Sciences of United States of America,2002,99(20):12589-12593.
[71] Cabrita G, Sousa P, Marques L. Player/Stage simulation of olfactory experiments[C].Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems,Taiwan: Taipei,2010:1120-1125.
[72] Lilienthal A, Duckett T. Building gas concentration gridmaps with a mobile robot[J]. Roboticsand Autonomous Systems,2004,48(1):3-16.
[73] Lilienthal A J, Reggente M, Trincavelli M, et al. A statistical approach to gas distributionmodelling with mobile robots-the kernel DM+V algorithm[C]. Proceedings of IEEE/RSJInternational Conference on Intelligent Robots and Systems, USA:ST Louis,2009:570-576.
[74] Kowadlo G, Russell R A. Using naive physics for odor localization in a cluttered indoorenvironments[J]. Automation Robot,2006,20(3):215-230.
[75] Ishida H, Nakamoto T, Moriizumi T. Remote sensing of gas/odor source location andconcentration distribution using mobile system[J]. Sensors and Actuators B: Chemical,1998,49(1-2):52-57.
[76] Ishida H, Nakamoto T, Moriizumi T. Remote sensing and localization of gas/odor source anddistribution using mobile sensing system[C]. Proceedings of International Conference onSolid State Sensors and Actuators, USA: Chicago,1997:559-562
[77] Lilienthal A, Ulmer H, Frohlich H, et al. Gas source declaration with a mobilerobot[C].Proceedings of IEEE International Conference on Robotics and Automation, Spain:Barcelona,2004:1430-1435.
[78] Lilienthal A, Duckett T. Creating gas concentration gridmaps with a mobilerobot[C].Proceedings of the IEEE/RSJ International Conference on Intelligent Robots andSystems, USA: Las Vegas,2003:118-123.
[79] Li W. Abstraction of odor source declaration algorithm from moth-inspired plume tracingstrategies[C]. Proceedings of IEEE International Conference on Robotics and Biomimetics,China: Kunming,2006:1024-1029.
[80] Li J G, Meng Q H, Wang Y, et al. Single odor source declaration in outdoor time-variantairflow environments[C]. Proceedings of the2010IEEE International Conference on Roboticsand Biomimetics, China: Tianjin,2010:143-148.
[81] Kowadlo G，Rawlinson D， Russell R A，et al. Bi-Modal search using complementarysensing (Olfaction/Vision) for odour source localization[C]. Proceedings of IEEEInternational Conference on Robotics and Automation, USA:Florida,2006:2041-2046.
[82] Loutfi A, Coradeschi S. Smell, Think, Act: A cognitive robot discriminating odours[J].Autonomous Robot,2006,20(3):239-249.
[83] Ishida H, Yamanaka T, Kushida N, et al. Study of real-time visualization of gas/odor flowimages using gas sensor array[J]. Sensors and Actuators B,2000,65(1-3):14-16.
[84] Lilienthal A J, Ulmer H, Frohlich H, et al. Learning to detect proximity to a gas source with amobile robot[C]. Proceedings of IEEE Intelligent Robots and Systems, Japan:Sendai,2004:1444-1449.
[85] Ishida H, Wada Y, Matsukura H, Chemical sensing in robotic applications: a review[J]. IEEESensors Journal,2012,12(11):3163-3173.
[86] Zhang S Q, Xu D M. A Survey of Biologically inspired chemical plume tracking strategiesfor single robot in2-D turbulence dominated flow environments[C]. Proceedings ofIEEE/SICE International Symposium on System Integration, Japan:Kyoto,2011,348-353.
[87] Turduev M, Cabrita G, Kirtay M, et al. Experimental studies on chemical concentration mapbuilding by a multi-robot system using bio-inspired algorithms[J]. Autonomous Agents andMulti-Agent Systems,2012,1-29.
[88]李飞.湍动气流主控环境下多机器人气味源定位[D].天津:天津大学,2009.
[89]蒋萍.融合机器人视/嗅觉信息的气体泄露源定位[D].天津:天津大学,2010.
[90]李吉功.室外时变气流环境下机器人气味源定位[D].天津:天津大学,2010.
[91]路光达.基于仿生行为的移动机器人主动嗅觉技术研究[D].天津:河北工业大学,2010.
[92]韩冰.湍流环境下化学羽状物定位技术研究[D].哈尔滨:哈尔滨工程大学,2012.
[93]谭民,王硕.机器人技术研究进展[J].自动化学报,2013,39(7):963-972.
[94]倪春波,孔一斐,杨月全,等.粒子群优化及其在多机器人系统中的引用展望[J].中南大学学报(自然科学版),2013,44(s2):126-131.
[95]刘华军,杨静宇,陆建峰,等.移动机器人运动规划研究综述[J].中国工程科学，2006,8(1)：85-94.
[96] Masehian E, Sedighizadeh D. Classic and heuristic approaches in robot motion planning–achronological review[C]. Proceedings of World Academy of Science, Engineering andTechnology, India:Raygada,2007:101-106.
[97] Shi Y H, Eberhart R C. A modified particle swarm optimizer [C]. Proceedings of the IEEECongress on Evolutionary Computation, USA:Anchorage,1998:69-73.
[98] Hao Y, Zu W, Zhao Y. Real-time obstacle avoidance method based on polar coordinationparticle swarm optimization in dynamic environment[C]. Proceedings of the2nd IEEEConference on Industrial Electronics and Applications, China:Harbin,2007:1612-1617.
[99]祖伟,李刚,齐正霞.基于改进粒子群优化算法的路径规划方法研究[J].弹箭与制导学报，2008,28(4):68-70.
[100]孙波,陈卫东,席裕庚.基于粒子群优化算法的移动机器人全局路径规划[J].控制与决策,2005,20(9):1052-1060.
[101] Xue Y H, Tian G H, Huang B.Optimal robot path planning based on danger degreemap[C]. Proceedings of IEEE International Conference on Automation and Logistics,China:Shenyang,2009:1040-1045.
[102] Ma Q Z, Lei X J, Zhang Q. Mobile robot path planning with complex constraints basedon the second-order oscillating particle swarm optimization algorithm[C]. Proceedings ofWorld Congress on Computer Science and Information Engineering, USA:Los Angeles,2009:244-248.
[103]陈德宝,赵春霞.基于内分泌调节机制的粒子群算法[J].控制理论与应用,2007,24(6):1005-1009.
[104]高晓巍.基于量子行为粒子群优化算法的路径规划[J].科技通报,2013,29(7):143-146.
[105]谭冠政,刘关俊.基于粒子群算法的移动机器人全局最优路径规划[J].计算机应用研究,2007,24(11):210-212.
[106]王艳,朱庆保.基于二进制粒子群算法的移动机器人路径规划[J].南京师范大学学报(工程技术版),2009，9(2):72-78.
[107] Wei L S, Dai X F. Particle swarm optimization based on cloud theory for robot pathplanning[J]. ICIC Express Letters Part B: Applications,2013,4(3):633-638.
[108]张超,李擎,陈鹏,等.一种基于粒子群参数优化的改进蚁群算法及其应用[J].北京科技大学学报,2013,35(7):955-960.
[109] Zhang Q R, Gu G C. Path planning based on improved binary particle swarmoptimization algorithm[C]. Proceedings of the2008IEEE International Conference onRobotics, Automation and Mechatronics, China:Chendu,2008:462-466.
[110] Hu C Y, Wu X N, Liang Q Z, et al. Autonomous robot path planning based on swarmintelligence and stream functions[J]. Lecture Notes in Computer Science,2007,4684:277-284.
[111] Hereford J M, Siebold M, Nichols S. Using the particle swarm optimization algorithmfor robotic search applications[C]. Proceedings of the IEEE Swarm Intelligence Symposium,USA:Honolulu,2007:53-59.
[112] Bashyal S, Venayagamoorthy G K. Human swarm interaction for radiation sourcesearch and localization[C]. Proceedings of IEEE Swarm Intelligence Symposium,USA:St.Louis,2008:1-8.
[113] Li G S, Shi H B. Path planning for mobile robot based on particle swarmoptimization[C]. Chinese Control and Decision Conference, China:Yantai,2008:3290-3294.
[114] Kang Hwan, Lee Byunghee, Kim Kabi. Path planning algorithm using the particleswarm optimization and the improved Dijkstra algorithm[C]. Proceedings of the2008Pacific-Asia Workshop on Computational Intelligence and Industrial Application,China:Wuhan,2008:1002-1004.
[115]秦元庆,孙德宝,李宁,等.基于粒子群算法的移动机器人路径规划[J].机器人,2004,26(3):222-225.
[116]昝杰,蔡宗琰,梁虎,等.基于Bezier曲线的自主移动机器人最优路径规划[J].兰州大学学报(自然科学版),2013,49(2):249-254.
[117] Tang T. Path planning of motion robot based on particle swarm optimization and FSVM[J]. International Journal of Advancements in Computing Technology,2012,4(21):642-649.
[118] Kennedy J, Mendes R. Population structure and particle swarm performance[C].Proceedings of the IEEE International Conference on Evolutionary Computation,USA:Hawaii,2002:1671-1676.
[119] Kennedy J, Mendes R. Neighborhood topologies in fully-informed and best-of-neighborhood particle swarms[C]. Proceedings of the2003IEEE International Workshop onSoft Computing in Industrial Application, USA:New York,2003:45-50.
[120] Ghosh P, Zafar H, Das S, et al. Hierarchical dynamic neighborhood based particleswarm optimization for global optimization [C]. IEEE Congress on EvolutionaryComputation, USA:New Orleans,2011:757-764.
[121] Khatib O. Real-time obstacle avoidance for manipulators and mobilerobots[J].International Journal of Robotics Research,1986,5(1):90-98.
[122] Roads J, Escudero C J, Iglesia D I. Bayesian filtering for a bluetooth positioningsystem[C].Proceedings of IEEE International Symposium on Wireless CommunicationSystems, Iceland:Reykjavik,2008:618-622.
[123]殷剑宏,吴开亚.图论及其算法[M].合肥:中国科学技术大学出版社,2003.
[124] Lu T F. Indoor odour source localisation using robot: initial location and surge distancematter?[J]. Robotics and Autonomous Systems,2013,61(6):637-647.
[125]韩占忠,王敬,兰小平.FLUENT——流体工程仿真计算实例与应用（第2版）[M].北京:北京理工大学出版社,2010.
[126]周勇,巩敦卫.交互式遗传算法的噪声及降噪策略[J].控制理论与应用,2008,25(2):221-227.
[127] Pan H, Wang L, Liu B. Particle swarm optimization for function optimization in noisyenvironment[J]. Applied Mathematics and Computation,2006,18(12):908-919.
[128] Janson S, Middendorf M. A hierarchical particle swarm optimizer for noisy anddynamic environments[J]. Genetic Programming and Evolvable Machines,2006,7(4):329-354.
[129] Rada-Vilela J, Zhang M J, Johnston M. Resampling in particle swarm optimization[C].2013IEEE Congress on Evolutionary Computation, Mexico: Cancun,2013:947-954.
[130] Han L, He X. A novel opposition-based particle swarm optimization for noisyproblems[C]. Proceedings of the Third International Conference on Natural Computation,China: Haikou,2007:624-629.
[131] Mendel E, Krohling R A, Campos M. Swarm algorithms with chaotic jumps applied tonoisy optimization problems[J]. Information Science,2010,181(20):4494-4514.
[132]张敏慧,马龙华,郑泳凌,等.区间数不等式约束不确定系统的线性规划[J].运筹与管理,2005,14(2):32-36.
[133] Sengupta A, Pal T K, Chakraborty D. Interpretion of inequality constraints involvinginterval coefficients and a solution to interval linear programming[J]. Fuzzy Sets and Systems,2001,119(1):119-129.
[134]张勇,巩敦卫,张芹英,等.带区间约束不确定优化问题的确定化描述[J]．系统工程理论与实践,2009,29(2):127-133.
[135] Kennedy J. Bare bones particle swarms[C]. Proceedings of the2003IEEE SwarmIntelligence Symposium, USA:Indianapolis,2003:80-87.
[136] Poli R, Langdon W B. Markov chain models of Bare-bones particle swarmoptimizers[C]. Genetic and Evolutionary Computation Conference, England: London,2007:142-149.
[137] Wang P P, Shi L P, Zhang Y, et al. A hybrid simplex search and modified bare-bonesparticle swarm optimization[J]. Chinese Journal of Electronics,2013,22(1):104-108.
[138] Das S, Maity S, Qu B Y, et al. Real-parameter evolutionary multimodal optimization-asurvey of the state-of-the-art[J]. Swarm and Evolutionary Computation,2011,1(2):71-88.
[139] Parrott D, Li X D. Locating and tracking multiple dynamic optima by a particle swarmmodel using speciation [J]. IEEE Transactions on Evolutionary Computation,2006,10(4):440-458.
[140] Yang S X, Li C H. A clustering particle swarm optimizer for locating and trackingmultiple optima in dynamic environments [J]. IEEE Transactions on EvolutionaryComputation,2010,14(6):959-974.
[141] Qu B Y, Suganthan P N, Das S. A distance-based locally informed particle swarm modelfor multimodal optimization [J]. IEEE Transactions on Evolutionary Computation,2013,17(3):387-402.
[142] Li X D. Niching without niching parameters: particle swarm optimization using a ringtopology [J]. IEEE Transactions on Evolutionary Computation,2010,14(1):150-169.
[143] Afsahi Z, Meybodi M R. A new approach on particle swarm optimization formultimodal functions [C]. Proceedings of the IEEE International Conference on IntelligentComputing and Intelligent Systems, China: Shanghai,2009:303-308.
[144] Jatmiko W, Nugraha A, Effendi R, et al. Localizing multiple odor sources in a dynamicenvironment based on modified niche particle swarm optimization with flow of wind[J].Wseas Transactions on Systems,2009,11(8):1187-1196.
[145] Goldberg D E, Richardson J. Genetic algorithm with sharing for multimodal functionoptimization[C]. Proceedings of the2nd International Conference on Genetic Algorithmsand their application, USA: Hillsdale,1987:41-49.
[146] Kennedy J. Stereotyping: improving particle swarm performance with clusteranalysis[C]. Proceedings of the Congress on Evolutionary Computation, New Jersey:Piscataway,2000:1507–1512.
[147] Parsopoulos K E, Plagianakos V P, Magoulas G D, et al. Stretching technique forobtaining global minimizers through particle swarm optimization[C]. Proceedings of theParticle Swarm Optimization Workshop, USA: Indianapolis,2001:22-29.
[148] Li X. Adaptively choosing neighborhood bests using species in a particle swarmoptimizer for multimodal function optimization[J]. Lecture Notes in Computer Science,2004,3102:105-116.
[149] Brits R, Engelbrecht A P, van den Bergh F. Locating multiple optima using particleswarm optimization[J]. Applied Mathematics and Computation,2007,189(2):1859-1883.
[150] Li J P, Wood A. Random search with species conservation for multimodal functions[C].Proceedings of the Eleventh Conference on Congress on Evolutionary Computation, Norway:Trondheim,2009:3164-3171.
[151]章军.小生境粒子群优化算法及其在多分类器集成中的应用研究[D].合肥:中国科技大学,2007.
[152] Brits R, Engelbrecht A, Bergh F D. A niching particle swarm optimizer[C]. Proceedingsof the4th Asia-Pacific Conference on Simulated Evolution and Learning2002, USA: Orchild,2002:692-696.
[153] Nickabadi A, Ebadzadhe M M, Safabakhsh R. A dynamic niching particle swarmoptimizer for multi-modal optimization[C]. Proceedings of the Congress on EvolutionaryComputation, China: Hong Kong,2008:26-32.
[154] Li X. Multimodal function optimization based on fitness Euclidean-distance ratio[C].Proceedings of the Genetic and Evolutionary Computation Conference, England: London,2007:78-85.
[155] Schoeman I, Engelbrecht A P. Using vector operations to identify niches for particleswarm optimization[C]. Proceedings of IEEE Conference on Cybernetics and IntelligentSystems, Singapore,2004:361-366.
[156] Branke J. Memory enhanced evolutionary algorithms for changing optimizationproblems[C]. Proceedings of IEEE Congress on Evolutionary Computation, USA:Washington,1999:1875-1882.
[157] Hu X, Eberhart R. Adaptive particle swarm optimization: detection and response todynamic systems[C]. Proceedings of IEEE Congress on Evolutionary Computation, USA:Hawaii,2002:1666-1670.
[158] Carlisle A, Dozier G. Adapting particle swarm optimization to dynamic environments[C].Proceedings of International Conference on Artificial Intelligence, USA: Las Vegas,2000:429-434.
[159] Singh H K, Isaacs A, Nguyen T T, et al. Performance of infeasibility driven evolutionaryalgorithm (IDEA) on constrained dynamic single objective optimization problems[C].Proceedings of IEEE Congress on Evolutionary Computation, Norway:Trondheim,2009:3127-3134.
[160] Li X, Branke J, Blackwell T. Particle swarm with speciation and adaptation in a dynamicenvironment[C]. Proceedings of Genetic and Evolutionary Computation Conference, USA:New York,2006:51-58.
[161] Vavak F, Jukes K A, Fogarty T C. Performance of a genetic algorithm with variable localsearch range relative to frequency for the environmental changes[C]. Proceedings ofInternational Conference on Genetic Programming, USA:Morgan Kaufmann,1998:22-25.
[162] Janson S, Middendorf M. A hierarchical particle swarm optimizer for noisy and dynamicenvironments[J]. Genetic Programming and Evolvable Machines,2006,7(4):329-354.
[163] Yang S, Yao X. Experimental study on population-based incremental learningalgorithms for dynamic optimization problems[J]. Soft Computing A-Fusion of Foundations,Methodologies and Applications,2005,9(11):815-834.
[164] Yang S. On the design of diploid genetic algorithms for problem optimization indynamic environments[C]. Proceedings of IEEE Congress on Evolutionary Computation,Canada: Vancouver,2006:1362-1369.
[165] Daneshyari M, Yen G. Dynamic optimization using cultural based PSO[C]. Proceedingsof IEEE Congress on Evolutionary Computation, USA: New Orleans,2011:509-516.
[166] Mavrovouniotis M, Yang S. Memory-based immigrants for ant colony optimization inchanging environments[C]. Proceedings of the International Conference on Applications ofEvolutionary Computation, Germany: Berlin,2011:324-333.
[167] Simoes A, Costa E. Improving prediction in evolutionary algorithms for dynamicenvironments[C]. Proceedings of Genetic and Evolutionary Computation Conference, USA:New York,2009:875-882.
[168] Fernandez J L, Arcos J L. Adapting particle swarm optimization in dynamic and noisyenvironments[C]. Proceedings of IEEE Congress on Evolutionary Computation, Spain:Barcelona,2010:765-772.
[169]陈莉,丁立新.动态优化算法综述[J].武汉大学学报(理学版),2011,57(3):255-264.
[170]陈杰,潘峰,王光辉.粒子群优化方法在动态优化中的研究现状[J].智能系统学报,2009,4(3):189-197.
[171] Dae II J, Young-Oh K. Combining single-value streamflow forecasts-A review andguidelines for selecting techniques[J]. Journal of Hydrology,2009,37(3-4):284-299.
[172] Fu T C. A review on time series data mining[J]. Engineering Applications of ArtificialIntelligence,2011,24(1):164-181.
[173] Marvuglia A, Messineo A. Using recurrent artificial neural networks to forecasthousehold rlectricity consumption[J]. Energy Procedia,2012,(14):45-55.
[174] Arindam C, Kajal D. Fuzzy support vector machine for bankruptcy prediction[J].Applied Soft Computing,2011,11(2):2472-2486.
[175]安德洪,韩文秀,岳毅宏.组合预测法的改进及其在负荷预测中的应用[J].系统工程与电子技术,2004,26(6):842-844.
[176]张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):32-42.
[177] Shawe-Taylor J, Sun S L.A review of optimization methodologies in support vectormachines[J]. Neurocomputing,2011,74(17):3609-3618.
[178] Hsu C W, Chang C C, Lin C J. A practical guide to support vector classification[EB/OL].http://www. csie. ntu.edu. tw/~cjlin/papers/guide/guide. pdf,2003-08-10.
[179]肖建,于龙,白裔峰.支持向量回归中核函数和超参数选择方法综述[J].西南交通大学学报,2008,43(3):297-303.
[180]王书舟,伞冶.支持向量机的训练算法综述[J].智能系统学报,2008,3(6):467-475.
[181]丁世飞,齐炳娟,谭红艳.支持向量机理论与算法研究综述[J].电子科技大学学报,2011,40(1):2-10.
[182] Reyes-Sierra M, Coello Coello C A. Multi-objective particle swarm optimizers: a surveyof the state-of-the-art [J]. International Journal of Computational Intelligence Research,2006,2(3):287-308.
[183] Shi Y, Eberhart R C. Particle swarm optimization with fuzzy adaptive inertia weight[C].Proceedings of the Workshop on Particle Swarm optimization2001, USA: Indianapolis,2001:101-106.
[184] Cai Z X, Wang Y. A multi-objective optimization-based evolutionary algorithm forconstrained optimization [J]. IEEE Transactions on Evolutionary Computation,2006,10(6):658-674.
[185] Woldesenbet Y G, Tessema B G, Yen G G. Constraint handling in multi-objectiveevolutionary optimization [C]. Proceedings of the IEEE Congress on EvolutionaryComputation, Singapore,2007:3077-3084.
[186] Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-II [J]. IEEE Transactions on Evolutionary Computations,2002,6(2):182-197.
[187] Shi Y, Eberhart R C. Empirical study of particle swarm optimization [C]. Proceedings ofthe IEEE Congress on Evolutionary Computation, USA: Washington,1999:1945-1950.
[188] Zitzler E, Thiele L. Multiobjective evolutionary algorithm: a comparative case study andthe strength pareto approach [J]. IEEE Transactions on Evolutionary Computations,1999,3(4):257-271.
[189] Zitzler, E, Deb K, Thiele L. Comparison of multi-objective evolutionary algorithms:empirical results[J]. Evolutionary Computation,2000,8(2):173-195.
[190] Wolpert D H, Macready W G. No free lunch theorems for optimization[J]. IEEETransactions on Evolutionary Computation,1997,1(1):67-82.