关键词: Agent, 模拟退火, 粒子群优化, 反向学习, 并行计算

Abstract: Traditional simulated annealing (SA) algorithm suffers from the problems of slow convergence, lack of memory in random sample and dependence on specific issues. This paper proposes a parallel SA algorithm based on particle swarm optimization (PSO) algorithm to solve the problems. By including the individual’s memory of PSO, the proposed algorithm can enhance the exploitation capability. In order to maintain diversity, a new opposition rotation learning strategy is introduced on the basis of opposition-based learning (OBL) algorithm. With the natural parallelism of PSO, the shortcoming of problem-dependence of SA is solved. In addition, the proposed algorithm runs on cluster to achieve coevolutions. Experiments conducted with protein structure prediction based on Toy models show that the proposed algorithm outperforms both in convergence speed and solution quality

Key words: Agent, simulated annealing, particle swarm optimization, opposition-based learning, parallel computing