Hybrid evolutionary optimization algorithm MPSO-SA

This paper proposes a new method for a modified particle swarm optimization algorithm (MPSO) combined with a simulated annealing algorithm (SA). MPSO is known as an efficient approach with a high performance of solving optimization problems in many research fields. It is a population intelligence al...

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Bibliographic Details
Published in:International journal for simulation and multidisciplinary design optimization 2010-01, Vol.4 (1), p.27-32
Main Authors: El Hami, N., Ellaia, R., Itmi, M.
Format: Article
Language:English
Subjects:
Birds
Computer simulation
evolutionary algorithm
Evolutionary algorithms
Global optimization
Hybrid Methods
Optimization algorithms
Particle swarm optimization
Performance enhancement
PSO
Robustness (mathematics)
Simulated annealing
Simulation
Swarm intelligence
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Summary:This paper proposes a new method for a modified particle swarm optimization algorithm (MPSO) combined with a simulated annealing algorithm (SA). MPSO is known as an efficient approach with a high performance of solving optimization problems in many research fields. It is a population intelligence algorithm inspired by social behavior simulations of bird flocking. Considerable research work on classical method PSO (Particle Swarm Optimization) has been done to improve the performance of this method. Therefore, the proposed hybrid optimization algorithms MPSO-SA use the combination of MPSO and simulated annealing SA. In this matter, a benchmark of eighteen well-known functions is given. These functions present different situations of finding the global minimum with gradual difficulties. Numerical results presented, in this paper, show the robustness of the MPSO-SA algorithm. Numerical comparisons with these three algorithms : Simulated Annealing, Modified Particle swarm optimization and MPSO-SA prove that the hybrid algorithm offers best results.
ISSN:1779-627X
1779-6288
1779-6288
DOI:10.1051/ijsmdo/2010004