新型随机分形搜索算法
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摘要
针对随机分形搜索算法在更新阶段中存在收敛速度慢、求解精度不高和易陷入局部最优等缺陷,提出一种新型随机分形搜索算法。通过将差分进化算法的变异操作引入到随机分形搜索算法的更新阶段,进一步增加生成群体的多样性并提高算法的求解精度,有效提高算法的搜索性能。采用12个标准测试函数进行数值实验,将新型随机分形算法与随机分形搜索算法和引力搜索算法进行比较。实验结果表明,新型随机分形搜索算法具有良好的优化性能。
To solve the problem of slow convergence speed,low accuracy and easily falling into local optimum in the updating process of stochastic fractal search algorithm(SFS),a kind of novel stochastic fractal search algorithm(NSFS)was proposed.By introducing the mutation operator of the differential evolution algorithm into the updating process of SFS,the diversity of the generated population and the accuracy of the algorithm were increased,thus the search performance of the algorithm was effectively improved.Series of computational experiments on 12 benchmark instances were tested and the comparisons with that of SFS and gravitational search algorithm show the NSFS has better performance.
To solve the problem of slow convergence speed,low accuracy and easily falling into local optimum in the updating process of stochastic fractal search algorithm(SFS),a kind of novel stochastic fractal search algorithm(NSFS)was proposed.By introducing the mutation operator of the differential evolution algorithm into the updating process of SFS,the diversity of the generated population and the accuracy of the algorithm were increased,thus the search performance of the algorithm was effectively improved.Series of computational experiments on 12 benchmark instances were tested and the comparisons with that of SFS and gravitational search algorithm show the NSFS has better performance.
引文
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[17]LI Mudong,ZHAO Hui,WENG Xingwei,et al.Differential evolution based on optimal Gaussian random walk and individual selection strategies[J].Control and Decision,2016,31(8):1379-1386(in Chinese).[李牧东,赵辉,翁兴伟,等.基于最优高斯随机游走和个体筛选策略的差分进化算法[J].控制与决策,2016,31(8):1379-1386.]
[2]WANG Chao,QIAO Junfei.An parameter adaptive particle swarm optimization for optimal design of water supply systems[J].CAAI Transactions on Intelligent Systems,2015,10(5):722-728(in Chinese).[王超,乔俊飞.参数自适应粒子群算法的给水管网优化研究[J].智能系统学报,2015,10(5):722-728.]
[3]Li Jianping,Li Qiqi,Yang Yun,et al.An artificial bee colony algorithm for multi-objective optimization[J].Applied Soft Computing,2017,50:235-251.
[4]YUAN Yabo,LIU Yi,WU Bin.Solving shortest path problem with modified ant colony algorithm[J].Computer Engineering and Applications,2016,52(6):8-12(in Chinese).[袁亚博,刘羿,吴斌.改进蚁群算法求解最短路径问题[J].计算机工程与应用,2016,52(6):8-12.]
[5]LIU Yong,MA Liang.Gravitational search algorithm and its application[M].Shanghai:Shanghai People Press,2014:10-21(in Chinese).[刘勇,马良.引力搜索算法及其应用[M].上海:上海人民出版社,2014:10-21.]
[6]LAN Shaofeng,LIU Sheng.Overview of research on cuckoo search algorithm[J].Computer Engineering and Design,2015,36(4):1063-1067(in Chinese).[兰少峰,刘升.布谷鸟搜索算法研究综述[J].计算机工程与设计,2015,36(4):1063-1067.]
[7]Aflakparast M,Salimi H.Cuckoo search epistasis:A new method for exploring significant genetic interactions[J].Heredity,2014,112(6):666-674.
[8]Liao Tianjun,Socha Krzysztof,Montes de Oca,et al.Ant colony optimization for mixed-variable optimization problems[J].IEEE Transactions on Evolutionary Computation,2014,18(4):503-518.
[9]WANG Wenxian,CHEN Dingjun,CHEN Bingyang.Locomotive working problem based on integrating of GA-ACO[J].Computer Simulation,2015,32(3):183-185(in Chinese).[王文宪,陈钉均,陈冰洋.基于遗传蚁群算法的机车周转优化[J].计算机仿真,2015,32(3):183-185.]
[10]WANG Hao,OUYANG Haibin,GAO Liqun.An improved global particle swarm optimization[J].Control and Decision,2016,31(7):1161-1168(in Chinese).[王皓,欧阳海滨,高立群.一种改进的全局粒子群优化算法[J].控制与决策,2016,31(7):1161-1168.]
[11]LIU Yong,MA Liang.Sine cosine algorithm with nonlinear decreasing conversion parameter[J].Computer Engineering and Applications,2017,53(2):1-5(in Chinese).[刘勇,马良.转换参数非线性递减的正弦余弦算法[J].计算机工程与应用,2017,53(2):1-5.]
[12]ZHAO Hongxing,CHANG Xiaogang.Improvement of artificial bee colony algorithm[J].Computer Engineering and Design,2018,39(1):260-265(in Chinese).[赵红星,常小刚.人工蜂群算法的改进[J].计算机工程与设计,2018,39(1):260-265.]
[13]Hamid Salimi.Stochastic fractal search:A powerful metaheuristic algorithm[J].Knowledge-Based Systems,2015,75(C):1-18.
[14]Fan Qinqin,Yan Xuefeng.Differential evolution algorithm with self-adaptive strategy and control parameters for P-xylene oxidation process optimization[J].Soft Computing,2015,19:1363-1391.
[15]GAO Yuelin,LIU Junmin.Parameter study of differential evolution algorithm[J].Journal of Natural Science of Heilongjiang University,2009,26(1):81-85(in Chinese).[高岳林,刘军民.差分进化算法的参数研究[J].黑龙江大学自然科学学报,2009,26(1):81-85.]
[16]MA Wei,SUN Zhengxing,LI Junlou.Cuckoo search algorithm based on Powell local search method for global optimization[J].Application Research of Computers,2015,32(6):1667-1674(in Chinese).[马卫,孙正兴,李俊楼.基于Powell局部搜索策略的全局优化布谷鸟算法[J].计算机应用研究,2015,32(6):1667-1674.]
[17]LI Mudong,ZHAO Hui,WENG Xingwei,et al.Differential evolution based on optimal Gaussian random walk and individual selection strategies[J].Control and Decision,2016,31(8):1379-1386(in Chinese).[李牧东,赵辉,翁兴伟,等.基于最优高斯随机游走和个体筛选策略的差分进化算法[J].控制与决策,2016,31(8):1379-1386.]