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

This paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. When a local op-timal 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]