Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems

作     者：Abd-El-Wahed, W. F. Mousa, A. A. El-Shorbagy, M. A. 

作者机构：Shebin El Kom Minufiya Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm Egypt Shebin El Kom Minufiya Univ Fac Comp & Informat Shibin Al Kawm Egypt 

出 版 物：《JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS》 (计算与应用数学杂志)

年 卷 期：2011年第235卷第5期

页      面：1446-1453页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

主　　题：Particle swarm optimization Genetic algorithm Nonlinear optimization problems Constriction factor 

摘      要：Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The aim of this paper is to introduce a hybrid approach combining two heuristic optimization techniques, particle swarm optimization (PSO) and genetic algorithms (GA). Our approach integrates the merits of both GA and PSO and it has two characteristic features. Firstly, the algorithm is initialized by a set of random particles which travel through the search space. During this travel an evolution of these particles is performed by integrating PSO and GA. Secondly, to restrict velocity of the particles and control it, we introduce a modified constriction factor. Finally, the results of various experimental studies using a suite of multimodal test functions taken from the literature have demonstrated the superiority of the proposed approach to finding the global optimal solution. (C) 2010 Elsevier B.V. All rights reserved.