基于动态种群和广义学习的粒子群算法及应用

A Particle Swarm Optimizer and Its Application Based on Dynamic Population and Comprehensive Learning

为了提升粒子跳出局部最优解的能力,本文提出一种动态种群和广义学习粒子群算法(DCPSO)。在算法运行过程中,引入种群增加策略和减少策略以提升种群的多样性,进而提升粒子跳出局部最优解的能力;同时引入广义学习策略以增加粒子飞向全局最优位置的概率。在基准函数的测试中,结果显示DCPSO算法比其它PSO算法有更好的性能;在实际应用中,通过对起重机箱型主梁模型进行优化,结果显示DCPSO算法比其它算法获得了质量更高的解。

In order to improve the ability to escape from local optima,we present an improved particle swarm optimizer based on dynamic population and comprehensive learning（DCPSO for short）.In DCPSO,the swarm population growing and declining strategies are introduced to increase the swarm diversity,further improve the ability to escape from local optima;a comprehensive learning strategy also is used to improve the probability of flying to the global best position.In the benchmark function,the results demonstrate good performance of the DCPSO algorithm in solving complex multimodal problems when compared with other PSO variants.In the optimization design for the box grider of portal gantry,the experimental results show that the DCPSO algorithm can achieve better solutions that other PSOs.