The 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 HPSO-SA, 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 HPSO-SA algorithm is validated on ten standard benchmark multimodal functions for which we obtained significant improvements. The results are compared with these obtained by existing hybrid PSO-SA algorithms. In this paper, we provide also two versions of HPSO-SA (sequential and distributed) for minimizing the energy consumption in embedded systems memories. The two versions, of HPSO-SA, reduce the energy consumption in memories from 76% up to 98% as compared to Tabu Search (TS). Moreover, the distributed version of HPSO-SA provides execution time saving of about 73% up to 84% on a cluster of 4 PCs.
A parallel evolutionary algorithm to optimize dynamic data types in embedded systems
