An Improved Hybrid Method Combining Gravitational Search Algorithm With Dynamic Multi Swarm Particle Swarm Optimization

作     者：Nagra, Arfan Ali Han, Fei Ling, Qing-Hua Mehta, Sumet 

作者机构：Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Key Lab Secur Technol Ind Cyberspace C-212013 Zhenjiang Jiangsu Peoples R China Jiangsu Univ Sci & Technol Sch Comp Sci & Engn Zhenjiang 212003 Jiangsu Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2019年第7卷

页      面：50388-50399页

核心收录：

基　　金：National Natural Science Foundation of China [61572241, 61271385] National Key Research and Development Program of China [2017YFC0806600] Foundation of the Peak of Six Talents of Jiangsu Province [2015-DZXX-024] Fifth 333 High Level Talented Person Cultivating Project of Jiangsu Province [(2016) III-0845] Research Innovation Program for College Graduates of Jiangsu Province [KYLX-1056] 

主　　题：Gravitational search algorithm dynamic multi swarm optimization neighborhood strategy benchmark optimization problems 

摘      要：GSA is badly suffering from a slow convergence rate and poor local search ability when solving complex optimization problems. To solve this problem, a new hybrid population-based algorithm is proposed with the combination of dynamic multi swarm particle swarm optimization and gravitational search algorithm (GSADMSPSO). The proposed algorithm has divided the main population of masses into smaller sub-swarms and also stabilizing them by presenting a new neighborhood strategy. Then, by adopting the global search ability of the proposed algorithm, each agent (particle) improves the position and velocity. The main idea is to integrate the ability of GSA with the DMSPSO to enhance the performance of exploration and exploitation of a proposed algorithm. In order to evaluate the competences of the proposed algorithm, benchmark functions are employed. The experimental results have been confirmed a better performance of GSADMSPSO as compared with the other gravitational and PSO variants in terms of fitness rate.