摘要
当最优解偏离目标函数定义域的几何中心时,反向个体容易远离全局最优解,基于反向差分进化算法的性能会大幅降低.该文引入基于当前最优解的反向学习策略,并与差分进化算法相结合,求解函数优化问题.当前代的最优解作为候选解和相应反向个体之间的对称点,能保证反向种群的利用率始终维持在较高水平.实验结果表明,该算法可行而高效,且算法性能的提升完全是反向个体的贡献.此外,提出一种增强的基于反向差分进化算法,展示出此类优化方法的最优效果.
When the global optimum is not located at the geometric center of the domain,the opposite numbers may lapse from the global optimum,leading to poor performance of opposition-based differential evolution.A novel opposition-based learning strategy using the current optimum is introduced,and it is combined with differential evolution for function optimization.The optimum in the current generation is served as a symmetry point between an estimate and the corresponding opposite estimate,resulting in a high rate of opposite population usage.Experiments results clearly show that the proposed algorithm can significantly improve the performance due to the opposite numbers.Additionally,an enhanced version of opposition-based differential evolution is proposed to reveal ideal and perfect results using opposition-based learning.
出处
《应用科学学报》
EI
CAS
CSCD
北大核心
2011年第3期308-315,共8页
Journal of Applied Sciences
基金
国家自然科学基金(No.60802056
No.61073091)
陕西省自然科学基金(No.2010JM8028)
西安理工大学优秀博士学位论文研究基金(No.105-211010)资助
关键词
差分进化
基于反向学习
当前最优解
函数优化
differential evolution
opposition-based learning
current optimum
function optimization
