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

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:235

Article

Abd-El-Wahed, W. F.²  Mousa, A. A.¹  El-Shorbagy, M. A.¹ 

[1] Shebin El Kom Minufiya Univ, Fac Engn, Dept Basic Engn Sci, Shibin Al Kawm, Egypt

[2] Shebin El Kom Minufiya Univ, Fac Comp & Informat, Shibin Al Kawm, Egypt

关键词: Particle swarm optimization;    Genetic algorithm;    Nonlinear optimization problems;    Constriction factor;   

DOI  :  10.1016/j.cam.2010.08.030

来源: Elsevier

【 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.

【 授权许可】

Free