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

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...

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Veröffentlicht in:Journal of computational and applied mathematics Jg. 235; H. 5; S. 1446 - 1453
Hauptverfasser: Abd-El-Wahed, W.F., Mousa, A.A., El-Shorbagy, M.A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier B.V 2011
Elsevier
Schlagworte:
Algorithms
Calculus of variations and optimal control
Constriction factor
Evolution
Exact sciences and technology
Genetic algorithm
Genetic algorithms
Heuristic
Mathematical analysis
Mathematical models
Mathematics
Nonlinear optimization problems
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Numerical methods in optimization and calculus of variations
Optimization
Particle swarm optimization
Sciences and techniques of general use
Constriction factor
Genetic algorithm
Nonlinear optimization problems
Particle swarm optimization
Optimization method
Nonlinear problems
Global solution
Experimental result
Numerical analysis
Applied mathematics
Optimal solution
Problem solving
Variational calculus
Mathematical programming
ISSN:0377-0427, 1879-1778
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-2
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ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2010.08.030