基于改进邻域搜索策略的人工蜂群算法
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摘要
针对人工蜂群算法存在易陷入局部最优、收敛速度慢的缺陷,提出一种改进邻域搜索策略的人工蜂群算法.首先,将混沌思想和反向学习方法引入初始种群,设计混沌反向解初始化策略,以增大种群多样性,增强跳出局部最优的能力;然后,在跟随蜂阶段根据更新前个体最优位置引入量子行为模拟人工蜂群获取最优解,通过交叉率设计更新前个体最优位置,并利用势阱模型的控制参数提高平衡探索与开发的能力,对观察蜂邻域搜索策略进行改进,以提高算法的收敛速度和精度;最后,将改进人工蜂群算法与粒子群算法、蚁群算法以及其他改进人工蜂群算法进行比较,利用12个标准测试函数进行仿真分析.结果表明,改进算法不仅提高了收敛速度和精度,而且在高维函数优化方面具有一定的优势.
As to overcome the drawback of easily falling into local optimum and slow convergence rate of the conventional artificial bee colony algorithm, this paper proposes an artificial bee colony algorithm based on the improved neighborhood search strategy. Firstly, in order to enhance the diversity of population and prevent local optimum, a kind of chaotic anti-base initialization mechanism is designed according to the chaotic thoughts and opposed-based learning method.Then, in the following stage of following bee stage, the quantum behavior is introduced to simulate the optimal solution of the artificial bee according to the optimal position of the former individual, the optimal position of the former individual is designed with crossover, and the control parameters of the well model is used to improve the balance exploration and development capability, a strategy of neighborhood search improvement strategy in observation is designed, to improve the convergence accuracy of the algorithm, Finally, the proposed algorithm is compared with the particle swarm optimization algorithm, ant colony algorithms, and other improved artificial colony algorithm, and simulation analysis is made on12 standard test functions, the results show that the proposed algorithm not only improves the convergence speed and accuracy, but also has certain advantages in terms of high-dimensional function optimization.
As to overcome the drawback of easily falling into local optimum and slow convergence rate of the conventional artificial bee colony algorithm, this paper proposes an artificial bee colony algorithm based on the improved neighborhood search strategy. Firstly, in order to enhance the diversity of population and prevent local optimum, a kind of chaotic anti-base initialization mechanism is designed according to the chaotic thoughts and opposed-based learning method.Then, in the following stage of following bee stage, the quantum behavior is introduced to simulate the optimal solution of the artificial bee according to the optimal position of the former individual, the optimal position of the former individual is designed with crossover, and the control parameters of the well model is used to improve the balance exploration and development capability, a strategy of neighborhood search improvement strategy in observation is designed, to improve the convergence accuracy of the algorithm, Finally, the proposed algorithm is compared with the particle swarm optimization algorithm, ant colony algorithms, and other improved artificial colony algorithm, and simulation analysis is made on12 standard test functions, the results show that the proposed algorithm not only improves the convergence speed and accuracy, but also has certain advantages in terms of high-dimensional function optimization.
引文
[1] Karaboba D. An idea based on honey bee swarm for numerical optimization[R]. Kayseri:Faculty of Engineering, Erciyes University, 2005.
[2] Dervis Karaboga, Bahriye Akay. A comparative study of artificial bee colony algorithm[J]. Applied Mathematics and Computation, 2009, 214(1):108-132.
[3] Shi Y J, Pun C M, Hu H D, et al. An improved artificial bee colony and its application[J]. Knowledge-Based Systems,2016, 107(5):14-31.
[4]王永琦,吴飞,孙建华.求解连续空间优化问题的改进蜂群算法[J].计算机应用研究, 2017, 35(3):1008-1013.(Wang Y Q, Wu F, Sun J H. Improved swarm optimization algorithm for continuous spatial optimization problems[J]. Application Research of Computer, 2017, 35(3):1008-1013.)
[5] Cai S H, Long W, Jiao J J. Hybridizing artificial bee colony with biogeography-based optimization for constrained mechanical design problems[J]. Springer,2015, 22(6):2250-2259.
[6] Xu H D, Jiang M Y, Xu K. Archimedean copula estimation of distribution algorithm based on artificial bee colony algorithm[J]. IEEE Systems Engineering and Electronics,2015, 26(2):388-396.
[7] Huang L P, Zhang B, Yuan X, et al. Solving service selection problem based on a novel multi-objective artificial bees colony algorithm[J]. Shanghai Jiaotong University, 2017, 22(4):474-480.
[8]毕晓君,王艳娇.加速收敛的人工蜂群算法[J].系统工程与电子技术, 2011, 33(12):2755-2761.(Bi X J, Wang Y Q. Artificial swarm algorithm for accelerating convergence[J]. Systems Engineering and Electronics Technology, 2011, 33(12):2755-2761.)
[9] Shimipi S J, Ritu T, Harism S, et al. Hybrid artificial bee colony algorithm with differential evolution[J]. Applied Soft Computing, 2017, 58(5):11-24.
[10] Weifeng G, Lingling H, Jue W, et al. Enhanced artificial bee colony algorithm through differential evolution[J].Applied Soft Computing, 2016, 48(6):137-150.
[11] Kiran M S, Hakli H, Gunguz M. Artificial bee colony algorithm with variable search strategy for continuous optimization[J]. Information Sciences, 2015, 300(1):140-157.
[12] Gu W J, Yu Y G, Hu W. Artificial bee colony algorithm-based parameter estimation of fractional-order chaotic system with time delay[J]. IEEE/CAA J of Automatica Sinica, 2017, 4(1):107-113.
[13] Ajit K, Dharmender K, Jarial S K. A novel hybrid K-means and artificial bee colony algorithm approach for data clustering[J]. Decision Science Letters, 2018,7(1):65-76.
[14] Changsheng Z, Dantong O Y, Jiaxu N. An artificial bee colony approach for clustering[J]. Expert Systems with Applications, 2010, 37(7):4761-4767.
[15]王志刚,尚旭东,夏慧明,等.多搜索策略协同进化的人工蜂群算法[J].控制与决策, 2018, 33(2):235-241.(Wang Z G, Shang X D, Xia H M, et al. Artificial bee colony algorithm with multiple-search strategies cooperative evolutionary[J]. Control and Decision, 2018,33(2):235-241.)
[16] Li M D, Zhao H, Wang X W, et al. Artificial bee colony algorithm with comprehensive search mechanism for numerical optimization[J]. IEEE Systems Engineering and Electronics, 2015, 26(3):603-617.
[17]李国亮,魏振华,徐蕾.分阶段搜索的改进人工蜂群算法[J].计算机应用, 2015, 35(4):1057-1061.(Li G L, Wei Z H, Xu L. The improved artificial swarm algorithm for the phased search[J]. Computer Application, 2015, 35(4):1057-1061.)
[18]王建,丁学明,董新燕.基于量子策略的布谷鸟搜索算法研究[J].电子科技, 2015, 28(12):40-44.(Wang J, Ding X M, Dong X Y. Research of cuckoo search algorithm based on quantum strategy[J]. Electronic Science and Technology, 2015, 28(12):40-44.)
[19]王冰.基于局部最优解的改进人工蜂群算法[J].计算机应用研究, 2014, 31(4):1023-1026.(Wang B. Improved artificial swarm algorithm based on local optimal solution[J]. Computer Application Research, 2014, 31(4):1023-1026.)
[20] Guopu Z, Sam K. Gbest-guided artificial bee colony algorithm for numerical function optimization[J].Applied Mathematics and Computation, 2010, 217(7):3166-3173.
[21]罗钧,肖向海,付丽,等.基于分段搜索策略的改进蜂群算法[J].控制与决策, 2012, 27(9):1402-1406.(Luo J, Xiao X H, Fu L, et al. Improved swarm algorithm based on segmented search strategy[J]. Control and Decision, 2012, 27(9):1402-1406.)
[22] Dervis K, Beyza G. A quick artificial bee colony(qABC)algorithm and its performance on optimization problems[J]. Applied Soft Computing, 2014, 23(5):227-238.
[2] Dervis Karaboga, Bahriye Akay. A comparative study of artificial bee colony algorithm[J]. Applied Mathematics and Computation, 2009, 214(1):108-132.
[3] Shi Y J, Pun C M, Hu H D, et al. An improved artificial bee colony and its application[J]. Knowledge-Based Systems,2016, 107(5):14-31.
[4]王永琦,吴飞,孙建华.求解连续空间优化问题的改进蜂群算法[J].计算机应用研究, 2017, 35(3):1008-1013.(Wang Y Q, Wu F, Sun J H. Improved swarm optimization algorithm for continuous spatial optimization problems[J]. Application Research of Computer, 2017, 35(3):1008-1013.)
[5] Cai S H, Long W, Jiao J J. Hybridizing artificial bee colony with biogeography-based optimization for constrained mechanical design problems[J]. Springer,2015, 22(6):2250-2259.
[6] Xu H D, Jiang M Y, Xu K. Archimedean copula estimation of distribution algorithm based on artificial bee colony algorithm[J]. IEEE Systems Engineering and Electronics,2015, 26(2):388-396.
[7] Huang L P, Zhang B, Yuan X, et al. Solving service selection problem based on a novel multi-objective artificial bees colony algorithm[J]. Shanghai Jiaotong University, 2017, 22(4):474-480.
[8]毕晓君,王艳娇.加速收敛的人工蜂群算法[J].系统工程与电子技术, 2011, 33(12):2755-2761.(Bi X J, Wang Y Q. Artificial swarm algorithm for accelerating convergence[J]. Systems Engineering and Electronics Technology, 2011, 33(12):2755-2761.)
[9] Shimipi S J, Ritu T, Harism S, et al. Hybrid artificial bee colony algorithm with differential evolution[J]. Applied Soft Computing, 2017, 58(5):11-24.
[10] Weifeng G, Lingling H, Jue W, et al. Enhanced artificial bee colony algorithm through differential evolution[J].Applied Soft Computing, 2016, 48(6):137-150.
[11] Kiran M S, Hakli H, Gunguz M. Artificial bee colony algorithm with variable search strategy for continuous optimization[J]. Information Sciences, 2015, 300(1):140-157.
[12] Gu W J, Yu Y G, Hu W. Artificial bee colony algorithm-based parameter estimation of fractional-order chaotic system with time delay[J]. IEEE/CAA J of Automatica Sinica, 2017, 4(1):107-113.
[13] Ajit K, Dharmender K, Jarial S K. A novel hybrid K-means and artificial bee colony algorithm approach for data clustering[J]. Decision Science Letters, 2018,7(1):65-76.
[14] Changsheng Z, Dantong O Y, Jiaxu N. An artificial bee colony approach for clustering[J]. Expert Systems with Applications, 2010, 37(7):4761-4767.
[15]王志刚,尚旭东,夏慧明,等.多搜索策略协同进化的人工蜂群算法[J].控制与决策, 2018, 33(2):235-241.(Wang Z G, Shang X D, Xia H M, et al. Artificial bee colony algorithm with multiple-search strategies cooperative evolutionary[J]. Control and Decision, 2018,33(2):235-241.)
[16] Li M D, Zhao H, Wang X W, et al. Artificial bee colony algorithm with comprehensive search mechanism for numerical optimization[J]. IEEE Systems Engineering and Electronics, 2015, 26(3):603-617.
[17]李国亮,魏振华,徐蕾.分阶段搜索的改进人工蜂群算法[J].计算机应用, 2015, 35(4):1057-1061.(Li G L, Wei Z H, Xu L. The improved artificial swarm algorithm for the phased search[J]. Computer Application, 2015, 35(4):1057-1061.)
[18]王建,丁学明,董新燕.基于量子策略的布谷鸟搜索算法研究[J].电子科技, 2015, 28(12):40-44.(Wang J, Ding X M, Dong X Y. Research of cuckoo search algorithm based on quantum strategy[J]. Electronic Science and Technology, 2015, 28(12):40-44.)
[19]王冰.基于局部最优解的改进人工蜂群算法[J].计算机应用研究, 2014, 31(4):1023-1026.(Wang B. Improved artificial swarm algorithm based on local optimal solution[J]. Computer Application Research, 2014, 31(4):1023-1026.)
[20] Guopu Z, Sam K. Gbest-guided artificial bee colony algorithm for numerical function optimization[J].Applied Mathematics and Computation, 2010, 217(7):3166-3173.
[21]罗钧,肖向海,付丽,等.基于分段搜索策略的改进蜂群算法[J].控制与决策, 2012, 27(9):1402-1406.(Luo J, Xiao X H, Fu L, et al. Improved swarm algorithm based on segmented search strategy[J]. Control and Decision, 2012, 27(9):1402-1406.)
[22] Dervis K, Beyza G. A quick artificial bee colony(qABC)algorithm and its performance on optimization problems[J]. Applied Soft Computing, 2014, 23(5):227-238.
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