基于子种群拉伸操作的精英共生生物搜索算法
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摘要
针对共生生物搜索算法存在易早熟、收敛速度慢等缺陷,提出一种基于子种群拉伸操作的精英共生生物搜索算法.在"互利共生"阶段,根据适应度值将种群划分为两个子种群,设计有针对性的进化策略,使两个子种群分别负责开发和探索,有效地平衡算法的收敛速度与精度;在"偏利共生"阶段,利用最优个体的方向性引导信息,引入拉伸因子和差分扰动向量,并修正个体更新模式,从而在提高算法收敛速度的同时保证种群的多样性;模拟寄生体和宿主的生物关系,提出精英"寄生"机制,进一步平衡算法在整个迭代过程中的探索与开发能力.对与标准共生生物算法、改进后的共生生物搜索算法以及其他4个群智能进化算法在17个函数上的测试结果进行比较分析,结果表明所提出的算法精度更佳,收敛速度优势明显.
An elite symbiotic organisms search(SOS) algorithm based on subpopulation stretching operation is proposed to solve the problems of premature and slow convergence in SOS. In the mutualism phase, the population is divided into two subpopulations according to the fitness value: One is responsible for development and the other is aimed at exploration. The targeted evolutionary strategy is designed for each subpopulation, which makes the algorithm keep a good balance between convergence speed and accuracy. In the commensalism phase, the individual updating mode is modifred by using the directional information of the optimal individual and introducing the stretching factor and the difference perturbation vector, which improves the convergence speed of the algorithm and guarantees the diversity of the population. The "parasitism" mechanism of the elite, which is proposed by simulating the biological relationships between parasites and hosts, keeps a further balance between development and exploration. The comparison examinations of the standard SOS, the improved SOS and other four intelligent evolutionary algorithms on 17 functions indicate that the proposed algorithm has better accuracy and obvious advantage of convergence speed.
An elite symbiotic organisms search(SOS) algorithm based on subpopulation stretching operation is proposed to solve the problems of premature and slow convergence in SOS. In the mutualism phase, the population is divided into two subpopulations according to the fitness value: One is responsible for development and the other is aimed at exploration. The targeted evolutionary strategy is designed for each subpopulation, which makes the algorithm keep a good balance between convergence speed and accuracy. In the commensalism phase, the individual updating mode is modifred by using the directional information of the optimal individual and introducing the stretching factor and the difference perturbation vector, which improves the convergence speed of the algorithm and guarantees the diversity of the population. The "parasitism" mechanism of the elite, which is proposed by simulating the biological relationships between parasites and hosts, keeps a further balance between development and exploration. The comparison examinations of the standard SOS, the improved SOS and other four intelligent evolutionary algorithms on 17 functions indicate that the proposed algorithm has better accuracy and obvious advantage of convergence speed.
引文
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[12]Sun W,Lin A,Yu H,et al.All-dimension neighborhood based particle swarm optimization with randomly selected neighbors[J].Information Sciences,2017405(C):141-156.
[2]Harik G R,Lobo F G,Goldberg D E.The compact genetic algorithm[J].IEEE Trans on Evolutionary Computation1999,3(4):287-297.
[3]Das S,Suganthan P N.Differential evolution:A survey of the state-of-the-art[J].IEEE Trans on Evolutionary Computation,2011,15(1):4-31.
[4]Bai Q.Analysis of particle swarm optimization algorithm[J].Computer&Information Science,20103(1):180-184.
[5]Ghosh A,Das S,Chowdhury A,et al.An improved differential evolution algorithm with fitness-based adaptation of the control parameters[J].Information Sciences,2011,181(18):3749-3765.
[6]Ishaque K,Salam Z,Amjad M,et al.An improved particle swarm optimization(PSO)-Based MPPT for PV with reduced steady-state oscillation[J].IEEE Trans on Power Electronics,2012,27(8):3627-3638.
[7]周虎,赵辉,李牧东,等.多策略自适应共生生物搜索算法[J].空军工程大学学报:自然科学版,2016,17(4):101-106.(Zhou H,Zhao H,Li M D,et al.Multi strategy adaptive symbiotic organisms search[J].J of Air Force Engineering University:Natural Science Edition,201617(4):101-106.)
[8]王艳娇,陶欢欢.基于旋转学习策略的共生生物搜索算法[J].计算机应用研究,2017,34(9):2614-2617.(Wang Y J,Tao H H.Symbiotic organisms search algorithm based on rotating learning strategy[J]Computer Application Research,2017,34(9):2614-2617.)
[9]Li X,Yin M.Modified differential evolution with self-adaptive parameters method[J].J of Combinatorial Optimization,2016,31(2):546-576.
[10]Xiao J,Niu Y,Chen P,et al.An improved gravitational search algorithm for green partner selection in virtual enterprises[J].Neurocomputing,2016,217:103-109.
[11]Ismail Babaoglu.Artificial bee colony algorithm with distribution-based update rule[J].Applied Soft Computing J,2015,34(C):851-861.
[12]Sun W,Lin A,Yu H,et al.All-dimension neighborhood based particle swarm optimization with randomly selected neighbors[J].Information Sciences,2017405(C):141-156.