文化粒子群优化算法

为了提高粒子群优化(PSO)算法的计算精度和计算效率,避免"早熟",给出了文化粒子群优化算法.该算法模型将PSO纳入文化算法框架,组成基于PSO的主群体空间和知识空间,两空间具有各自群体并独立并行演化.下层主群体空间定期贡献精英个体给上层知识空间,上层知识空间经演化后,定期贡献精英个体给下层主群体空间,于是形成"双演化双促进"机制,从而实现增加PSO的群体多样性.在以卫星舱和印刷电路板布局设计为背景的算例中进行了数值验证,结果表明对于该算例,该方法的计算精度和计算效率比遗传算法、PSO算法高.

Cultural-based particle swarm optimization algorithm

A cultural-based particle swarm optimization(CBPSO) algorithm is proposed to improve the computational accuracy and efficiency of PSO and avoid premature.This algorithm model consists of a PSO-based main population space and a knowledge space,which respectively has its own population to evolve independently and parallel.The lower level main population space(PSO population) contributes elite individuals to the upper level space(knowledge population) periodically,and the upper level space continually evolves these elite individuals and then contributes elite individuals to the lower level space.The mechanism of dual evolution and dual promotion improves the population diversity,and avoids premature.Two examples originated from the layout design of satellite module and integrated circuit show that CBPSO exhibits better computational efficiency and accuracy than GA and PSO.