Particle swarm optimization: Hybridization perspectives and experimental illustrations

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...

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Bibliographic Details
Published in:Applied mathematics and computation Vol. 217; no. 12; pp. 5208 - 5226
Main Authors: Thangaraj, Radha, Pant, Millie, Abraham, Ajith, Bouvry, Pascal
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Inc 15.02.2011
Elsevier
Subjects:
Adaptive algorithms
Algorithms
Benchmarking
Calculus of variations and optimal control
Differential evolution
Evolution
Exact sciences and technology
Genetic algorithms
Hybridization
Mathematical analysis
Mathematics
Nonlinear algebraic and transcendental equations
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
Differential evolution
Hybridization
Particle swarm optimization
Genetic algorithms
OR algorithm
Transcendental equation
Optimization method
Global solution
Non linear equation
Numerical analysis
Genetic algorithm
Applied mathematics
Classification
Algebraic equation
Problem solving
Variational calculus
Method study
Mathematical programming
ISSN:0096-3003, 1873-5649
Online Access:Get full text
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Summary: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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ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2010.12.053