Hybrid evolutionary optimization algorithm MPSO-SA

¹ Laboratory of Study and Research in Applied Mathematics (LERMA), Mohammed V University - Engineering Mohammedia School Rabta, BP 765 Ibn Sina avenue, Agdal, Morocco
² Laboratory of Rouen (LITIS), National Institute for Aplied Sciences - Rouen BP 08, University avenue 76801, St Etienne du Rouvray cedex, France

Corresponding author: Norelislam@hotmail.com

Received: 10 December 2009
Accepted: 15 February 2010

Abstract

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.