区域分割的自适应变异粒子群算法

发布时间：2018-04-08 21:10

本文选题：区域分割　切入点：信息交叉　出处：《电子学报》2017年08期

【摘要】：为了提高粒子群算法(PSO)的收敛性及多样性,提出一种基于区域分割的自适应变异粒子群算法(RSVPSO).算法采用区域分割的思想,利用粒子间信息交叉,使粒子搜索区间快速缩小;同时在迭代后期与自适应变异策略相结合,提高粒子跳出局部最优陷阱的能力和增强粒子多样性,达到寻优的目的.将所提出的算法应用于8个测试函数,并与精英免疫克隆选择的协同进化粒子群等算法进行比较,结果表明,新算法在收敛速度、搜索精度及寻优效率等方面有较大提高.
[Abstract]:In order to improve the convergence and diversity of particle swarm optimization (PSO), an adaptive mutation particle swarm optimization (RSVPSO) based on region segmentation is proposed.The algorithm adopts the idea of region segmentation and makes use of the intersection of information between particles to reduce the searching interval of particles quickly. At the same time, the algorithm combines with adaptive mutation strategy in the late iteration to improve the ability of particles to jump out of the local optimal trap and enhance the diversity of particles.To achieve the goal of searching for the best.The proposed algorithm is applied to eight test functions and compared with the co-evolutionary particle swarm optimization algorithm selected by elitist immune clone. The results show that the new algorithm can improve the convergence speed, search accuracy and optimization efficiency.
【作者单位】：湖北大学计算机与信息工程学院物联网工程研究所;黄冈师范学院电子信息学院;
【基金】：国家科技支撑计划(No.2015BAK03B02)
【分类号】：TP18

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