基于随机差分变异的改进鲸鱼优化算法
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摘要
针对基本鲸鱼优化算法(whale optimization algorithm,WOA)求解复杂问题时存在解精度低、收敛速度慢和易陷入局部最优的缺点,提出了一种基于非线性调整控制参数和随机差分变异策略的改进鲸鱼优化算法(IWOA),设计了基于余弦函数非线性调整控制参数的策略,以协调算法的探索和开发能力;随机选择个体与当前个体进行差分变异产生新个体以增强群体的多样性,减小陷入局部最优的概率。对6个标准测试函数进行仿真实验,结果表明,IWOA的寻优精度和收敛速度均有明显的提高。
To solve low precision,slow convergence and easy to fall into local optimum for solving complex problems in the basic whale optimization algorithm(WOA),an improved whale optimization algorithm(IWOA)based on nonlinear adjustment control parameter and stochastic differential mutation strategy is proposed.Firstly,a nonlinear adjustment control parameter strategy based on Cosine function is designed for coordinate exploration and exploitation.Then,a differential mutation strategy with the randomly selected individual and the current individual is introduced to enhance the diversity of population and reduce the probability of falling into a local optimum.Simulation experiments are conducted on six benchmark test functions.The results show that the IWOA has better performance in precision and convergence than other algorithms.
To solve low precision,slow convergence and easy to fall into local optimum for solving complex problems in the basic whale optimization algorithm(WOA),an improved whale optimization algorithm(IWOA)based on nonlinear adjustment control parameter and stochastic differential mutation strategy is proposed.Firstly,a nonlinear adjustment control parameter strategy based on Cosine function is designed for coordinate exploration and exploitation.Then,a differential mutation strategy with the randomly selected individual and the current individual is introduced to enhance the diversity of population and reduce the probability of falling into a local optimum.Simulation experiments are conducted on six benchmark test functions.The results show that the IWOA has better performance in precision and convergence than other algorithms.
引文
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[2]AZIZ M A E,EWEES A A,HASSANIEN A E.Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation[J].Expert Systems with Applications,2017,83:242-256.
[3]牛培峰,吴志良,马云鹏,等.基于鲸鱼优化算法的汽轮机热耗率模型预测[J].化工学报,2017,68(3):1049-1057.NIU Peifeng,WU Zhiliang,MA Yunpeng,et al.Prediction of steam turbine heat consumption rate based on whale optimization algorithm[J].CIESC Journal,2017,68(3):1049-1057.(in Chinese)
[4]MAFARJA M M,MIRJALILI S.Hybrid whale optimization algorithm with simulated annealing for feature selection[J].Neurocomputing,2017,260:302-312.
[5]徐继亚,王艳,纪志成.基于鲸鱼算法优化WKELM的滚动轴承故障诊断[J].系统仿真学报,2017,29(9):2189-2197.XU Jiya,WANG Yan,JI Zhicheng.Fault diagnosis method of rolling bearing based on WKELM optimized by whale optimization algorithm[J].Journal of System Simulation,2017,29(9):2189-2197.(in Chinese)
[6]LING Y,ZHOU Y,LUO Q.Lévy flight trajectorybased whale optimization algorithm for global optimization[J].IEEE Access,2017,5(99):6168-6186.
[7]HU H,BAI Y,XU T.Improved whale optimization algorithms based on inertia weights and theirs applications[J].International Journal of Circuits,Systems and Signal Processing,2017,11:12-26.
[8]郭振洲,王平,马云峰,等.基于自适应权重和柯西变异的鲸鱼优化算法[J].微电子学与计算机,2017,34(9):20-25.GUO Zhenzhou,WANG Ping,MA Yunfeng,et al.Whale optimization algorithm based on adaptive weight and Cauchy mutation[J].Microeletronics and Computer,2017,34(9):20-25.(in Chinese)
[9]龙文,蔡绍洪,焦建军,等.求解大规模优化问题的改进鲸鱼优化算法[J].系统工程理论与实践,2017,37(11):2983-2994.LONG Wen,CAI Shaohong,JIAO Jianjun,et al.Improved whale optimization algorithm for large scale optimization problems[J].Systems Engineering Theory&Practice,2017,37(11):2983-2994.(in Chinese)
[10]钟明辉,龙文.一种随机调整控制参数的鲸鱼优化算法[J].科学技术与工程,2017,17(12):68-73.ZHONG Minghui,LONG Wen,Whale optimization algorithm based on stochastic adjustment control parameter[J].Science Technology and Engineering,2017,17(12):68-73.(in Chinese)
[11]罗佳,唐斌.基于收敛因子非线性动态变化的灰狼优化算法[J].中国科技论文,2016,11(17):1991-1997.LUO Jia,TANG Bin.Grey wolf optimization algorithm based on nonlinear convergence factor dynamic changing[J].China Sciencepaper,2016,11(17):1991-1997.(in Chinese)