Swarm intelligence
Energy aware memory architecture configuration
MEDEA '04 Proceedings of the 2004 workshop on MEmory performance: DEaling with Applications , systems and architecture
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Particle swarm optimization with simulated annealing for TSP
AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6
Particle Swarm Based Meta-Heuristics for Function Optimization and Engineering Applications
CISIM '08 Proceedings of the 2008 7th Computer Information Systems and Industrial Management Applications
Convergence behavior of the fully informed particle swarm optimization algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A new hybrid algorithm of particle swarm optimization
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
A survey on optimization metaheuristics
Information Sciences: an International Journal
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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.