摘要
针对粒子群优化算法由于缺乏种群多样性而导致早熟收敛的不利因素.提出了一种把差异演化算法中的后代产生机制引入粒子群优化算法的更新规则中以保持粒子群的种群多样性和加快收敛速度的算法.这种思想能有效改善摆脱极值点的能力.基于几个高维测试函数的试验结果显示,该算法在收敛速度快和精度方面都优于粒子群优化算法.
Considering particle swarm optimization (PSO) being easily trapped in local optima because of the loss of population diversity, an algorithm that DE offspring generation scheme is introduced in the update rules of PSO in order to maintain the population diversity and accelerate the converging speed is proposed in this paper. This strategy can improve the ability of escaping the local optima effectively. Simulation results on a suite of benchmark functions show that the proposed algorithm is superior to original particle swarm optimization algorithm.
出处
《哈尔滨商业大学学报(自然科学版)》
CAS
2008年第3期290-292,319,共4页
Journal of Harbin University of Commerce:Natural Sciences Edition
基金
教育部新世纪优秀人才基金(NCET-05-0734)
广东省自然科学基金(04020079)
关键词
粒子群优化算法
差异演化算法
种群多样性
测试
全局连续优化
particle swarm optimization
differential evolution
diversity of population
testing
global continuous optimization