A novel hybrid evolutionary algorithm for multi-modal function optimization and engineering applications

Lhassane Idoumghar (1, 2) , Mohamed Melkemi (2) , René Schott (3, 4)

1 TRIO - Real time and interoperability
2 LMIA - Laboratoire de Mathématiques Informatique et Applications [UHA]
3 IECN - Institut Élie Cartan de Nancy
4 LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications

This paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes dif ficult . To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called PSOSA, based on the idea that PSO ensures fast convergence, while SA brings the search out of local optima because of its strong local-search ability. The proposed PSOSA algorithm is val idated on ten standard benchmark functions and two engi neering design problems. The numerical results show that our approach outperforms algorithms described in [1, 2].

Domaines: Probabilités [math.PR]

Lhassane Idoumghar, Mohamed Melkemi, René Schott. A novel hybrid evolutionary algorithm for multi-modal function optimization and engineering applications. 13th IASTED International Conference on Artificial Intelligence and Soft Computing - ASC 2009, Sep 2009, Palma de Mallorca, Spain. pp.87-93. ⟨hal-00431693⟩