摘要
针对粒子群算法存在的收敛速度较慢和早熟收敛两大难题提出了一种新的改进型粒子群算法:搜索初期由粒子群算法进行全局寻优,当判断粒子群体已经进入局部最优区域时,引入复合形算法迅速达到局部收敛,从而有效地提高粒子群算法的局部搜索能力。同时引入自适应变异惯性权重提高摆脱局部最优的能力,增加种群的多样性。通过典型优化函数的实验验证,该算法是一种兼顾局部性能和全局搜索能力的高效算法。
To deal with the problem Of premature convergence,slow convergence velocity,a novel Panicle Swarm Optimization （PSO ） algorithm is proposed.At the beginning of the evolution,PSO can search global area and find the local range quickly,and then,complex method would locate the extremum in the local range rapidly.The self-adaptive mutation inertia weight is used in the whole evolvement to break away from the local extremum,which can effectively solve the premature convergence problem.The experiment results of two classic benchmark functions show that the algorithm can not only significantly improve the convergence velocity and precision in the evolutionary optimization,but also effectively enhance the global optimization power.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第31期47-50,共4页
Computer Engineering and Applications
关键词
粒子群算法
复合形算法
自适应变异
Particle Swarm Optimization（PSO）
complex method
self-adaptive mutation
