Hybrid PSO‐SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems

Abstract

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.