An improved GA and a novel PSO-GA-based hybrid algorithm

Authors:
X. H. Shi;Y. C. Liang;H. P. Lee;C. Lu;L. M. Wang
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China and Institute of High Performance Computing, Singapore 117528, Singapore;Institute of High Performance Computing, Singapore 117528, Singapore;Institute of High Performance Computing, Singapore 117528, Singapore;College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Computer Science and Technology, Changchun Taxation College, Changchun 130021, China
Venue:
Information Processing Letters
Year:
2005

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Abstract

Inspired by the natural features of the variable size of the population, we present a variable population-size genetic algorithm (VPGA) by introducing the ''dying probability'' for the individuals and the ''war/disease process'' for the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, a novel PSO-GA-based hybrid algorithm (PGHA) is also proposed in this paper. Simulation results show that both VPGA and PGHA are effective for the optimization problems.