摘要
为了提高群集蜘蛛优化(SSO)算法的性能,提出一种基于动态学习策略的群集蜘蛛优化(DSSO)算法.该算法通过群体协作过程中学习因子的动态选择,平衡算法的搜索能力和勘探能力;采用随机交叉策略和云模型改进协作过程个体更新方式,在维持种群多样性的同时尽量提高收敛速度.基于标准测试函数的仿真实验表明,DSSO算法可有效避免早熟收敛,在收敛速度和收敛精度上较标准SSO算法和其余4种较具代表性的优化算法均有显著提高.
In order to improve the performance of social spider optimization（SSO） algorithm,a social spider optimization algorithm with the dynamic learning strategy（DSSO） is proposed.In this algorithm,a dynamic selection mechanism for the learning factor in population cooperation is applied to balance solution accuracy and search speed.A manner to update individual combining randomized crossover strategy and cloud theory is proposed to improve the collaboration manner,which can maintain the diversity of the population as much as possible and improve searching speed.Experimental results on benchmark functions show that the DSSO algorithm improves convergence property and robustness compared with the representative four algorithms.
出处
《控制与决策》
EI
CSCD
北大核心
2015年第9期1575-1582,共8页
Control and Decision
基金
国家自然科学基金项目(61175126)
东北电力大学博士科研启动基金项目(BSJXM-2013-20)
关键词
群集蜘蛛优化算法
函数优化
动态学习
social spider optimization algorithm
function optimization
dynamic learning strategy
