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

International audienceThis 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]