应用反向学习和差分进化的群搜索优化算法
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摘要
针对标准群搜索优化算法在解决一些复杂优化问题时容易陷入局部最优且收敛速度较慢的问题,提出一种应用反向学习和差分进化的群搜索优化算法(Group Search Optimization with Opposition-based Learning and Differential Evolution,OBDGSO)。该算法利用一般动态反向学习机制产生反向种群,扩大算法的全局勘探范围;对种群中较优解个体实施差分进化的变异操作,实现在较优解附近的局部开采,以改善算法的求解精度和收敛速度。这两种策略在GSO算法中相互协同,以更好地平衡算法的全局搜索能力和局部开采能力。将OBDGSO算法和另外4种群智能算法在12个基准测试函数上进行实验,结果表明OBDGSO算法在求解精度和收敛速度上具有较显著的性能优势。
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.
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.
引文
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[2]SAUNDERS J R,LI X.Application of a group search optimization based artificial neural network to machine condition monitoring[C]∥The 13th 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]∥International 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 2010Special Session and Competition on Large-Scale Global Optimization[D].Hefei:Nature Inspired Computation and Applications Laboratory,USTC,2009.