应用反向学习和差分进化的群搜索优化算法

Abstract

Abstract: In general,the standard group search optimization algorithm (GSO) is easy to fall into the local optimum and its convergence speed is slow when solving some complex optimization problems.A group search optimization algorithm based on opposition-based leaning and differential evolution (OBDGSO) was proposed in this paper.The OBDGSO uses the opposition-based learning operator to generate the opposite population to expand the global exploration range.In addition,the operator of differential evolution (DE) is utilized to perform local exploitation to improve the solution accuracy.These two strategies are integrated into the GSO to better balance the abilities of the global convergence and local search.The OBDGSO is tested on 12 benchmark functions along with four other peering algorithms,and the experimental results show that the OBDGSO has significant performance advantages in solution accuracy and convergence speed.

Key words: Differential evolution, Group search optimizationalgorithm, Opposition-based learning

CLC Number:

TP301

ZOU Hua-fu,XIE Cheng-wang,ZHOU Yang-ping,WANG Li-ping. Group Search Optimization with Opposition-based Learning and Differential Evolution[J].Computer Science, 2018, 45(6A): 124-129.

References

[1]HE S,WU Q H,SAUNDERS J R.A Novel Group Search Optimizer Inspired by Animal Behavioural Ecology[C]∥IEEE Congress on Evolutionary Computation.2006:1272-1278.
[2]SAUNDERS J R,LI X.Application of a group search optimization based artificial neural network to machine condition monitoring[C]∥The 13^th IEEE International Conference on Emerging Technologies and Factory Automation.Hamburg,2008:15-18.
[3]TANG W J,LI M S,HE S,et al.Optimal power flow with dynamic loads using bacterial foraging algorithm[C]∥Internatio-nal Conference on Power Systems Technology.2006,10:22-26.
[4]李丽娟,徐小通,刘锋.基于群智能的群搜索算法及其在离散变量设计中的应用[J].钢结构,2008(增刊):592-596.
[5]李丽娟,张雯雯,徐小通,等.改进的群搜索优化算法及其应用[J].空间结构,2016,16(2):13-24.
[6]庞艳娟.混合群搜索优化算法及其应用[D].太原:太原科技大学,2010.
[7]易卜拉欣.基于文化框架的群搜索和粒子群的混合算法及其应用[D].上海:华东理工大学,2014.
[8]汪慎文,丁立新,谢承旺,等.群搜索优化算法中角色分配策略的研究[J].小型微型计算机系统,2012,33(9):1938-1943.
[9]汪慎文,丁立新,谢大同,等.应用反向学习策略的群搜索优化算法[J].计算机科学,2012,39(9):183-187.
[10]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.
[11]王立平,谢承旺.一种带反向学习机制的自适应烟花爆炸算法[J].计算机科学,2016,43(11A):103-107.
[12]STORN R,PRICE K.Differential evolution:A simple and efficient adaptive scheme for global optimization over continuous spaces:Technical Report TR-95-012[R].ICSI,USA,1995.
[13]周新宇,吴志健,王晖,等.一种精英反向学习的粒子群优化算法[J].电子学报,2013,11(8):1647-1652.
[14]周新宇,吴志健,王明文.基于正交实验设计的人工蜂群算法[J].软件学报,2015,26(9):2167-2190.
[15]TANG K,LI X D,SUGANTHAN P N,et al.Benchmark Functions for the CEC’s 2010 Special Session and Competition on Large-Scale Global Optimization[D].Hefei:Nature Inspired Computation and Applications Laboratory,USTC,2009.

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Group Search Optimization with Opposition-based Learning and Differential Evolution

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