【摘要】 粒子群优化算法是一类简单有效的随机全局优化技术。该文把模拟退火思想引入到具有杂交和高斯变异的粒子群优化算法中,给出了一种基于模拟退火的粒子群优化算法。该算法基本保持了粒子群优化算法简单容易实现的特点,但改善了粒子群优化算法摆脱局部极值点的能力,提高了算法的收敛速度和精度。四个基准测试函数的仿真对比结果表明,该算法不仅增强了全局收敛性,而且收敛速度和精度均优于粒子群优化算法。更多还原

【Abstract】 Particle swarm optimizers are a simple stochastic glob al optimization technique.In this paper,the mechanism of simulated annealing is involved into original particle swarm optimizer with crossover and Gaussian muta tion,and a simulated annealing-based particle swarm optimization algorithm is proposed.The proposed algorithm is almost as simple for implement as particle swarm optimizer,but can improve the abilities of seeking the global excellent result and evolution speed.The experiment results demonstrate that the propos ed algorithm is superior to original particle swarm optimization algorithm.更多还原