摘要
为了提高布谷鸟搜索算法求解函数优化问题的求精能力和收敛速度,提出了一种基于自适应机制的改进算法。自适应机制用于控制缩放因子和发现概率,以提高种群的多样性,避免早熟,从而使更多的个体参与演化,达到提高求精能力和收敛速度的效果。仿真实验结果表明,与标准的布谷鸟搜索算法相比,基于自适应机制缩放因子的改进算法(rCS)和基于自适应机制发现概率的改进算法(paCS)在求精能力和收敛速度上都有明显的提高;同时具有自适应缩放因子和自适应发现概率的改进算法(iCS)比rCS和paCS具有更优的求精能力和收敛速度。
To improve the refining ability and convergence rate of cuckoo search algorithm for function optimization problems,an improved algorithm based on self-adaptive machine is proposed.The self-adaptive machine is used to control the scaling factor and find probability so as to improve population diversity and avoid premature,as a result,more individuals participating in the evolution,and then refining ability and convergence rate are improved.The results of experiment show the improved algorithm based on self-adaptive scaling factor （rCS）,and the improved algorithm based on self-adaptive finding probability （paCS） make great improvement on refining ability and convergence rate,comparing with the standard cuckoo search algorithm.They also suggest that the improved algorithm iCS based both on rCS and paCS has better refining ability and convergence rate than rCS and paCS.
出处
《计算机工程与设计》
CSCD
北大核心
2013年第10期3639-3642,共4页
Computer Engineering and Design
基金
福建省自然科学基金项目(2011J05044)
关键词
布谷鸟搜索算法
函数优化问题
自适应机制
求精能力
收敛速度
cuckoo search algorithm
function optimization problems
self-adaptive machine
refining ability
convergence rate
