A Cooperative Evolutionary Algorithm Based on Particle Swarm Optimization and Simulated Annealing Algorithm
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摘要: 提出了一种基于模拟退火与微粒群算法的协同进化方法,利用了微粒群算法的易实现性、局部快速收敛性以及模拟退火算法的全局收敛性.通过两种算法的协同搜索,可以有效克服微粒群算法的早熟收敛.仿真结果表明,本文的协同进化方法不仅具有较好的全局收敛性能,而且具有较快的收敛速度.文章从理论上证明了该方法以概率1收敛于全局最优解.Abstract: The paper proposes a cooperative evolutionary algorithm based on particle swarm optimization (PSO) and simulated annealing algorithm (SA). The method makes full use of the local convergent performance of PSO and the global convergent performance of SA, and can validly overcome the premature problem in PSO through cooperative search between PSO and SA. Experimental results show that the proposed algorithm owns a good globally convergent performance with a faster convergent rate. Moreover, theoretical analysis has been made to prove that the algorithm can converge to the global optimum with probability 1.
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