一种改进的进化模型和混沌优化的萤火虫算法

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英文篇名：Firefly Algorithm Based on Improved Evolutionary Model and Chaos Optimization 作者：李肇基 ; 程科 ; 王万耀 ; 崔庆华 英文作者：LI Zhaoji;CHENG Ke;WANG Wanyao;CUI Qinghua;School of Computer,Jiangsu University of Science and Technology; 关键词：萤火虫优化 ; 混沌种群 ; 惯性权重 ; 进化模式 ; 动态步长 ; 边界变异 英文关键词：firefly optimization;;chaotic population;;inertia weight;;evolutionary model;;dynamic step;;boundary mutation 中文刊名：JSSG 英文刊名：Computer & Digital Engineering 机构：江苏科技大学计算机学院; 出版日期：2019-07-20 出版单位：计算机与数字工程 年：2019 期：v.47;No.357 语种：中文; 页：JSSG201907010 页数：8 CN：07 ISSN：42-1372/TP 分类号：46-53

摘要

针对传统萤火虫算法在全局寻优搜索中存在收敛速度慢、求解精度低、易陷入局部极值区域等缺陷,提出一种改进的进化模型和混沌优化的萤火虫算法(FAEC)。首先,采用逻辑自映射产生混沌序列对萤火虫个体位置进行初始化,提高种群多样性;其次,在算法进化过程中引入惯性权重,控制前代个体对后代个体的影响,并利用种群最优个体的引导作用加强不同个体之间的信息共享;然后,引入动态步长与对称边界变异操作,解决越界问题并继续提高种群多样性。在6个标准测试函数上与传统萤火虫算法和基于改进进化机制的萤火虫算法进行了对比,实验结果表明,所提算法能有效避免陷入局部最优,具有更高的求解精度和更快的收敛速度。
        In order to overcome the disadvantages of firefly algorithm such as slow convergence speed,low computational accuracy and high possibility of being trapped in local optimum,a chaotic population firefly algorithm based on new evolutionary model is proposed. Firstly,chaotic sequence generated by the logical self-mapping is used to initialize individual position,which lays the foundation for the diversity of the population. Secondly,the inertia weight is used to control the influence of the previous generation position,and the population optimal individual is used to enhance the exchange of information between fireflies. Finally,the dynamic step size and symmetric boundary variation operations is introduced to control cross-border problems and improve the diversity of the population. The proposed algorithm is compared with standard firefly algorithm and firefly algorithm based on improved evolutionism on six benchmarks,and the results show that the proposed algorithm can not only obtain better solution accuracy and quicker convergence speed,but also avoid trapping in local optimum.

引文

[1]Yang X S. Nature-Inspired Metaheuristic Algorithms[M].Luniver Press,2008.
    [2]Yang X S. Firefly Algorithms for Multimodal Optimization[J]. Mathematics,2010,5792:169-178.
    [3]Yang X S,Deb S. Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization[J]. Studies in Computational Intelligence,2010,284:101-111.
    [4]Baykaso?lu A,Ozsoydan F B. An improved firefly algorithm for solving dynamic multidimensional knapsack problems[J]. Expert Systems with Applications,2014,41(8):3712-3725.
    [5]Shareef H,Ibrahim A A,Salman N,et al. Power quality and reliability enhancement in distribution systems via optimum network reconfiguration by using quantum firefly algorithm[J]. International Journal of Electrical Power&Energy Systems,2014,58(6):160-169.
    [6]Apostolopoulos T,Vlachos A. Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem[J]. International Journal of Combinatorics,2011,(2010-12-09),2010,2011(1687-9163).
    [7]Gandomi A H,Yang X S,Talatahari S,et al. Firefly algorithm with chaos[J]. Communications in Nonlinear Science&Numerical Simulation,2013,18(1):89-98.
    [8]冯艳红,刘建芹,贺毅朝.基于混沌理论的动态种群萤火虫算法[J].计算机应用,2013,33(3):796-799.FENG Yanhong,LIU Jianqin,HE Yizhao. Dynamic population firefly algorithm[J]. computer application based on chaos theory,2013,33(3):796-799.
    [9]符强,童楠,钟才明,等.基于改进型进化机制的萤火虫优化算法[J].计算机科学,2014,41(3):228-231.FU Qiang,TONG Nan,ZHONG Caiming,et al. Based on improved evolutionary mechanism of firefly optimization algorithm[J]. computer science,2014,41(3):228-231.
    [10]郁书好.萤火虫优化算法研究及应用[D].合肥:合肥工业大学,2015.YU Shuhao. Glowworm swarm optimization algorithm research and application[D]. Hefei:Hefei University of Technology,2015.
    [11]赵玉新,杨新社,刘利强.新兴元启发式优化方法[M].北京:科学出版社,2013.ZHAO Yuxin,YANG Xinshe,LIU Liqiang. The new heuristic optimization method[M]. Beijing:Science Press,2013.
    [12]刘长平,叶春明.具有混沌搜索策略的萤火虫优化算法[J].系统管理学报,2013,22(4):538-543.LIU Changping,YE Chunming. With chaotic searching swarm optimization algorithm for[J]. system management the journal,2013,22(4):538-543.
    [13]苏有良,周德俭,吴兆华,等.不同映射的混沌免疫进化算法性能分析[J].计算机工程,2010,36(21):222-224.SU Liang,ZHOU Dejian,WU Zhaohua,et al. Performance analysis of different mapped chaotic immune evolutionary algorithms[J]. Computer Engineering,2010,36(21):222-224.
    [14]陈寿文.基于质心和自适应指数惯性权重改进的粒子群算法[J].计算机应用,2015,51(3):58-64.CHEN Shouwen. Particle swarm optimization([J].)computer application based on centroid and adaptive exponential inertia weight improvement, 2015, 51(3):58-64.
    [15]臧睿,李辉辉.基于标准萤火虫算法的改进与仿真应用[J].计算机科学,2016,43(S2):113-116.ZANG Rui,LI Huihui. Application of improved and simulation based on standard firefly algorithm[J]. computer science,2016,43(S2):113-116.
    [16]宋莉,邓长寿,曹良林.粒子群优化算法的边界变异策略比较研究[J].计算机工程,2015,41(3):191-197.SONG Li,DENG Changshou,CAO Lianglin. Comparison of boundary variation strategies for particle swarm optimization(PSO)algorithm[J]. Computer Engineering,2015,41(3):191-197.