摘要
针对粒子群优化(PSO)算法的早熟收敛问题,提出一种新的基于群体多样性控制的PSO算法(DCPSO).该方法使得粒子在收缩状态下充分搜索,在发散状态下能够飞离群体的聚集位置,不断的收缩-发散过程保证了群体能在较大的空间进行搜索,减少了粒子群算法的早熟收敛现象.通过对多个标准测试函数的实验结果表明,DCPSO算法在复杂优化问题中具有较强的全局搜索能力,而且比现有的多样性指导的PSO算法(ARPSO)具有更好的性能.
Aiming at the premature convergence problem in particle swarm optimization （PSO） algorithm, a novel diversity-controlled PSO （DCPSO） algorithm is proposed. Guided by the controlled swarm＇s diversity, the particles search in the attractive phase sufficiently and adjust themselves by moving away from the center of the swarm quickly in the repulsive phase. The attractive-repulsive procedure can guarantee the population search in a wide space and help to avoid trapping into the local minima. Experimental results on several well-known benchmark functions show that DCPSO has strong global optimization ability in the complicated problems and outperforms the existing diversityguided PSO （ARPSO）.
出处
《控制与决策》
EI
CSCD
北大核心
2008年第8期863-868,共6页
Control and Decision
基金
国家自然科学基金项目(60474030)
关键词
粒子群优化
早熟收敛
多样性
全局收敛
Particle swarm optimization
Premature convergence
Diversity
Global convergence
