Modified particle swarm optimization algorithm with simulated annealing behavior and its numerical verification

The hybrid algorithm that combined particle swarm optimization with simulated annealing behavior (SA-PSO) is proposed in this paper. The SA-PSO algorithm takes both of the advantages of good solution quality in simulated annealing and fast searching ability in particle swarm optimization. As stochas...

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Bibliographic Details
Published in:Applied mathematics and computation Vol. 218; no. 8; pp. 4365 - 4383
Main Authors: Shieh, Horng-Lin, Kuo, Cheng-Chien, Chiang, Chin-Ming
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Inc 15.12.2011
Elsevier
Subjects:
Algorithms
Calculus of variations and optimal control
Computational efficiency
Computing time
Convergence
Elite reserve
Exact sciences and technology
Heuristic search
Mathematical analysis
Mathematical models
Mathematics
Metropolis process
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
Searching
Simulated annealing
Heuristic search
Particle swarm optimization
Simulated annealing
Metropolis process
Elite reserve
Optimization method
Algorithm
Convergence
Stochastic programming
Convergence speed
Mathematical function
Numerical analysis
Algorithm performance
Applied mathematics
Variational calculus
Mathematical programming
ISSN:0096-3003, 1873-5649
Online Access:Get full text
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Summary:The hybrid algorithm that combined particle swarm optimization with simulated annealing behavior (SA-PSO) is proposed in this paper. The SA-PSO algorithm takes both of the advantages of good solution quality in simulated annealing and fast searching ability in particle swarm optimization. As stochastic optimization algorithms are sensitive to their parameters, proper procedure for parameters selection is introduced in this paper to improve solution quality. To verify the usability and effectiveness of the proposed algorithm, simulations are performed using 20 different mathematical optimization functions with different dimensions. The comparative works have also been conducted among different algorithms under the criteria of quality of the solution, the efficiency of searching for the solution and the convergence characteristics. According to the results, the SA-PSO could have higher efficiency, better quality and faster convergence speed than compared algorithms.
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ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2011.10.012