一种改进的鲸鱼优化算法
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摘要
鲸鱼优化算法(WOA)是一种基于元启发式的群智优化算法,针对鲸鱼优化算法收敛速度精度低和容易陷入局部最优解的问题,提出一种基于反向学习的鲸鱼优化算法(OWOA)。采用改进的反向差分进化算法(IODE)替换随机产生的初始解以提高算法的全局收敛速度,在迭代过程中融合动态反向学习算法,增强算法的全局搜索能力,避免算法出现早熟现象。通过7个测试函数的仿真实验,证明该算法(OWOA)具有更好的收敛精度、收敛速度和稳定性。
Whale Optimization Algorithm(WOA) is a kind of swarm intelligence optimization algorithm based on meta-heuristic. Aiming at the prob-lem of low convergence speed and easy to fall into local optimal solution of whale optimization algorithm, proposes a whale optimization al-gorithm based on reverse learning(OWOA). The improved Opposition-based Differential Evolution(IODE) algorithm is used to replacethe randomly generated initial solution to improve the global convergence speed of the algorithm. The dynamic reverse learning algorithmis integrated in the iteration process to enhance the global search ability of the algorithm and avoid premature phenomenon of the algo-rithm. The simulation results of seven test functions show that the algorithm(OWOA) has better convergence accuracy, convergence speedand stability.
Whale Optimization Algorithm(WOA) is a kind of swarm intelligence optimization algorithm based on meta-heuristic. Aiming at the prob-lem of low convergence speed and easy to fall into local optimal solution of whale optimization algorithm, proposes a whale optimization al-gorithm based on reverse learning(OWOA). The improved Opposition-based Differential Evolution(IODE) algorithm is used to replacethe randomly generated initial solution to improve the global convergence speed of the algorithm. The dynamic reverse learning algorithmis integrated in the iteration process to enhance the global search ability of the algorithm and avoid premature phenomenon of the algo-rithm. The simulation results of seven test functions show that the algorithm(OWOA) has better convergence accuracy, convergence speedand stability.
引文
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[15]J.Tang,X.Zhao.On the Improvement of Opposition-Based Differential Evolution.In Natural Computation(ICNC),2010 Sixth International Conference.IEEE,2010,5:2407-2411.
[16]J.Digalakis,K.Margaritis.On Benchmarking Functions for Genetic Algorithms[J].Comput.Math.2001,77:481-506
[17]S.Mirjalili,A.Lewis.S-Shaped Versus V-Shaped Transfer Functions for Binary Particle Swarm Optimization[J].Swarm and Evolutionary Computation,2013,9:1-14.
[18]Yang X S.A New Metaheuristic Bat-Inspired Algorithm[J].Nature Inspired Cooperative Strategies for Optimization,2012,284:65-7.
[2]崔文东.鲸鱼优化算法在水库优化调度中的应用[J].水利水电科技进展,2017:72-76.
[3]刘竹松,等.正余混沌双弦鲸鱼优化算法[J].计算机工程与应用,2017:1-6.
[4]钟明辉.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,2017:68-73.
[5]Hof PR,Van Der Gucht E.Structure of the Cerebral Cortex of the Humpback Whale.Megapteanovaeangline(Cetacea,Mysticeti,Balaenopteridae)[J].AnatRec,2007,290:1-31.
[6]Goldbogen JA,Friedlaender AS,Calambokid sJ,et al.Integrative Approaches to the Study of Baleen Whale Diving Behavior,feeding Performance,and Foraging Ecology[J].Bio-Science,2013,63:90-100.
[7]Tizhoosh H.Opposition-Based Learning:A New Scheme for Machine Intelligence[C].Proceedings of the International Conference on Computational Intelligence for Modeling Control and Automation,2005:695-701.
[8]Wang Hui,Liu Y,Zeng S Y,et al.Opposition-Based Particle Swarm Algorithm With Cauchy Mutation[C].Proc.Congr.Evol.Comput.,2007:4750-4756.
[9]RahnamayanS,Tizhoosh H R,Salama M M.Opposition-Based Differential Evolution[J].IEEE Transaction on Evolutionary Computation,2008,12(1):64-79.
[10]Tizhoosh H,MalisiaA R.Applying Opposition-Based Ideas to the Ant Colony System[C].Proceedings of IEEE Swarm Intelligence Symposiu,2007:79-87.
[11]井福荣.一种使用反向学习策略的改进花粉授粉算法[J].江西理工大学学报,2015:101-106.
[12]汪慎文.应用反向学习策略的群搜索优化算法[J].计算机科学,2012:183-187.
[13]韩俊英.应用反向学习策略的果蝇优化算法[J].计算机应用与软件,2014:157-160.
[14]王晖.区域变换搜索的智能算法研究[D].武汉:武汉大学,2011.
[15]J.Tang,X.Zhao.On the Improvement of Opposition-Based Differential Evolution.In Natural Computation(ICNC),2010 Sixth International Conference.IEEE,2010,5:2407-2411.
[16]J.Digalakis,K.Margaritis.On Benchmarking Functions for Genetic Algorithms[J].Comput.Math.2001,77:481-506
[17]S.Mirjalili,A.Lewis.S-Shaped Versus V-Shaped Transfer Functions for Binary Particle Swarm Optimization[J].Swarm and Evolutionary Computation,2013,9:1-14.
[18]Yang X S.A New Metaheuristic Bat-Inspired Algorithm[J].Nature Inspired Cooperative Strategies for Optimization,2012,284:65-7.