L. Idoumghar, M. Melkemi, and R. Schott (France)
Particle Swarm Optimization, Simulated Annealing, Hybrid algorithms.
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 difficult . 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 validated on ten standard benchmark functions and two engineering design problems. The numerical results show that our approach outperforms algorithms described in [1, 2].
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