Development and validation of different hybridization strategies between GA and PSO

被引：52

作者：

Gandelli, A. ^{[1
]}

Grimaccia, F. ^{[1
]}

Mussetta, M. ^{[1
]}

Pirinoli, P. ^{[2
]}

Zich, R. E. ^{[1
]}

机构：

[1] Politecn Milan, Dipartimento Elettrotecn, Piazza Leonardo Vinci 32, I-20133 Milan, Italy

[2] Politecn Torino, Dipartimento Elettr, I-10129 Turin, Italy

来源：

2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS | 2007年

关键词：

D O I：

10.1109/CEC.2007.4424823

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper a new class of hybridization strategies between GA and PSO is presented and validated. The Genetical Swarm Optimization (GSO) approach is presented here with respect with different test cases to prove its effectiveness. GSO is a hybrid evolutionary technique developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches, the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA), but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO). The here proposed class of hybrid algorithms is tested for various benchmark problems, analyzing different computational costs, and finally reporting some numerical results.

引用

页码：2782 / +

页数：2

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