改进的鸡群优化算法
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摘要
针对鸡群优化算法中解的更新效率较低且缺乏探索性等问题,提出了一种改进的鸡群优化算法。该算法基于标准鸡群优化算法的种群分组更新机制,并借鉴狼群优化算法和粒子群优化算法的思想,引入改进因子和去重操作算子分别用以增强算法的寻优能力和提高种群的多样性。通过与其他4种算法在CEC 2014测试函数集上进行比较,结果表明本文算法在绝大多数测试函数上均表现出了良好的优化效果,在求解精度及收敛速度方面也优于其他算法。
Based on the hierarchy mechanism of the conventional chicken swarm optimization(CSO)algorithm,an improved chicken swarm optimization(ICSO)algorithm is proposed to enhance the solution accuracy and the convergence rate of the conventional CSO algorithm. The ICSO algorithm introduces several improved factors that learned from the grey wolf optimizer(GWO) and the particle swarm optimization(PSO),to extend the searching ability of the algorithm. Moreover,a duplicate remove operator is also introduced to improve the diversity of the population. Experimental results show that the accuracy of the solution and the convergence rate of the proposed algorithm are better than other benchmark algorithms.
Based on the hierarchy mechanism of the conventional chicken swarm optimization(CSO)algorithm,an improved chicken swarm optimization(ICSO)algorithm is proposed to enhance the solution accuracy and the convergence rate of the conventional CSO algorithm. The ICSO algorithm introduces several improved factors that learned from the grey wolf optimizer(GWO) and the particle swarm optimization(PSO),to extend the searching ability of the algorithm. Moreover,a duplicate remove operator is also introduced to improve the diversity of the population. Experimental results show that the accuracy of the solution and the convergence rate of the proposed algorithm are better than other benchmark algorithms.
引文
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[9]Wu Z,Yu D,Kang X.Application of improved chicken swarm optimization for MPPT in photovoltaic system[J].Optimal Control Applications and Methods,2018,39(2):1029-1042.
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[15]Liang Jing,Qu B Y,Suganthan Ponnuthurai N,et al.Problem definitions and evaluation criteria for the CEC 2013 special session and competition on single objective real-parameter numerical optimization[C]∥IEEE Congress on Evolutionary Computation,Beijing,China,2014.
[2]Banerjee S,Chattopadhyay S.Improved serially concatenated convolution turbo code(SCCTC)using chicken swarm optimization[C]∥IEEE Power,Communication and Information Technology Conference(PCITC),Bhubaneswar,India,2015.
[3]Hafez A I,Zawbaa H M,Emary E,et al.An innovative approach for feature selection based on chicken swarm optimization[C]∥IEEE International Conference on Soft Computing&Pattern Recognition,Tokyo,Japan,2016.
[4]Chen P,Mao Y.Wireless sensor network node localization algorithm based on chicken swarm optimization and multi-power mobile anchor[C]∥The 3rd International Conference on Materials Engineering,Manufacturing Technology and Control,Taiyuan,China,2016.
[5]Mu Y,Zhang L,Chen X,et al.Optimal trajectory planning for robotic manipulators using chicken swarm optimization[C]∥IEEE International Conference on Intelligent Human-machine Systems&Cybernetics,Chengdu,China,2016.
[6]Roslina,Zarlis M,Yanto I T R,et al.A framework of training ANFIS using chicken swarm optimization for solving classification problems[C]∥IEEE International Conference on Informatics&Computing,Mataram,Indonesia,2017.
[7]Yi Z,Liu J,Wang S,et al.PAPR reduction technology based on CSO algorithm in CO-OFDM system[C]∥The International Conference on Optical Communications&Networks,Wuzhen,China,2017.
[8]Wu Yu,Yan Bo,Qu Xiang-ju.Improved chicken swarm optimization method for reentry trajectory optimization[J].Mathematical Problems in Engineering,2018(2):1-13.
[9]Wu Z,Yu D,Kang X.Application of improved chicken swarm optimization for MPPT in photovoltaic system[J].Optimal Control Applications and Methods,2018,39(2):1029-1042.
[10]Han M,Liu S.An improved binary chicken swarm optimization algorithm for solving 0-1 Knapsack problem[C]∥The 13th International Conference on Computational Intelligence and Security(CIS),Hong Kong,2017.
[11]Yang,S X.Firefly algorithm,stochastic test functions and design optimisation[J].International Journal of Bio-inspired Computation,2010,2(2):7884.
[12]王沈娟,高晓智.萤火虫算法研究综述[J].微型机与应用,2015,34(8):8-11.Wang Shen-juan,Gao Xiao-zhi.A survey of firefly algorithm[J].Microcomputer and Its Applications,2015,34(8):8-11.
[13]Mirjalili S,Mirjalili S M,Lewis A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69(3):46-61.
[14]龙文,赵东泉,徐松金,等.求解约束优化问题的改进灰狼优化算法[J].计算机应用,2015,35(9):2590-2595.Long Wen,Zhao Dong-quan,Xu Song-jin,et al.Improved grey wolf optimization algorithm for constrained optimization problem[J].Journal of Computer Applications,2015,35(9):2590-2595.
[15]Liang Jing,Qu B Y,Suganthan Ponnuthurai N,et al.Problem definitions and evaluation criteria for the CEC 2013 special session and competition on single objective real-parameter numerical optimization[C]∥IEEE Congress on Evolutionary Computation,Beijing,China,2014.