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Zbl 1203.65089
Abd-El-Wahed, W.F.; Mousa, A.A.; El-Shorbagy, M.A.
Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems. (English)
[J] J. Comput. Appl. Math. 235, No. 5, 1446-1453 (2011). ISSN 0377-0427

Summary: 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.

MSC 2000:: *65K05 Mathematical programming (numerical methods)
90C15 Stochastic programming
90C59 Approximation methods and heuristics

Keywords: particle swarm optimization; genetic algorithm; nonlinear optimization problems; constriction factor; numerical examples; heuristic optimization

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Zentralblatt MATH Berlin [Germany]