摘要
针对人工蜂群算法存在收敛速度慢、易早熟等缺点,提出一种改进的人工蜂群算法.利用随机动态局部搜索算子对当前的最优蜜源进行局部搜索,以加快算法的收敛速度;同时,采用基于排序的选择概率代替直接依赖适应度的选择概率,维持种群的多样性,以避免算法出现早熟收敛.对标准测试函数的仿真实验结果表明,所提出的算法具有较快的收敛速度和较高的求解精度.
Taking into account the basic artificial bee colony algorithm converges slowly and prematurely, an improved artificial bee colony algorithm based on local search is proposed. The method makes full use of the stochastic dynamic local search to optimize the current best solution to speed up the convergence rate. In order to maintain the population diversity and avoid premature convergence, the selection probability based on ranking is used instead of depending on fitness directly. Through the simulation experiment on a suite of standard functions, the results show that the algorithm has a faster convergence rate and higher solution accuracy.
出处
《控制与决策》
EI
CSCD
北大核心
2014年第1期123-128,共6页
Control and Decision
基金
国家自然科学基金项目(60974082)
中央高校基本科研业务费专项资金项目(K5051270002)
西安电子科技大学基本科研业务项目(K5051270013)
关键词
人工蜂群
局部搜索算子
排序选择
函数优化
artificial bee colony
local search
rank selection
function optimization
