一种改进的群搜索优化算法-《郑州大学学报(工学版)》

文章信息/Info

Author(s):: AN Xiao-wei1; SU Hong-sheng1; 2; 1.Sehool of Automation and Electrical Engineering,Lanzhou Jiaotong University ,Lanzhou 730070，China; 2.School of Auto-mation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070，China

Keywords:: group searching optimization; function optimization; artificial fish swarm algorithm

摘要:: 群搜索优化算法是建立在群居动物觅食行为基础上的新型启发式算法,具有算法简单、易于实现的特点.标准群搜索优化算法(CSO)基于发现–追随的寻优策略,由于追随者搜索模式过于单一,从而容易陷入局部最优.为了提高标准GSO算法的收敛速度与收敛精度,提出一种改进群搜索优化算法(IGSO).在该算法中,发现者保持原有的寻优方式,追随者执行鱼群算法的寻优模式,通过引入鱼群算法的觅食、追尾、聚群与随机行为,使搜索方式多样化,可以同时考虑种群的个体最优与群体最优,从而有效避免陷入局部最优.通过6个基准测试函数对两种算法进行比较,实验结果表明,改进的群搜索优化算法优于标准群搜索优化算法.

Abstract:: Group Search Optimization (GSO) is a swarm intelligence approach inspired by animal searchingbehavior and group living theory. It is simple and efficient , and easy to implement. The searching mode of thescrounger is oversimplified , so it falls into local optimum easily. In order to enhance its convergence speed andprecision，the improved Group Search Optimization ( IGS0) is proposed. Inheriting the strategy of producer-scrounger of GSO，IGSO introduces the strategy of the Antificial Fish Swarm (AFS) algorithm to the behaviorof the scrounger. By introducing prey , fellow , swarm and leap of the AFS algorithm ， searching forms is diver-sified，as well as the best individuals of group and best groups of population can be considered，IGS0 can ef-ectively avoid the local optimum. Six benchmark functions are used to evaluate the performance of two algo-rithms..Experimental results.show that ICSO.is able to achieve better results than standard GSo.