Particle swarm optimization: Hybridization perspectives and experimental illustrations |
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Authors: | Radha Thangaraj Millie PantAjith Abraham Pascal Bouvry |
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Institution: | a Faculty of Science, Technology and Communications, University of Luxembourg, Luxembourg b Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee 247667, India c Machine Intelligent Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, USA |
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Abstract: | Metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. In the present study an attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic; particle swarm optimization or PSO. Hybridization is a method of combining two (or more) techniques in a judicious manner such that the resulting algorithm contains the positive features of both (or all) the algorithms. Depending on the algorithm/s used we made three classifications as (i) Hybridization of PSO and genetic algorithms (ii) Hybridization of PSO with differential evolution and (iii) Hybridization of PSO with other techniques. Where, other techniques include various local and global search methods. Besides giving the review we also show a comparison of three hybrid PSO algorithms; hybrid differential evolution particle swarm optimization (DE-PSO), adaptive mutation particle swarm optimization (AMPSO) and hybrid genetic algorithm particle swarm optimization (GA-PSO) on a test suite of nine conventional benchmark problems. |
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Keywords: | Particle swarm optimization Hybridization Differential evolution Genetic algorithms |
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