A firefly algorithm based on elite neighborhood guide
摘要:
由于萤火虫的移动采用的是全吸引模型,所以当迭代过程中有移动时,可能会存在振荡较大、时间计算复杂度较高等问题.为了克服这些不足,提出了一种基于精英邻居引导的萤火虫算法.算法通过利用精英邻居的信息引导萤火虫的移动,减少振荡的发生,降低时间计算复杂度.同时,若某只萤火虫周围不存在精英邻居,则利用自身的信息进行反向学习以提高算法跳出局部最优的能力.数值实验表明本文算法的鲁棒性、寻优精度及搜索速度均优于其他几种算法.
Since the full attraction model is used in FA,it may result in strong oscillations during the movement and high time complexity.In order to overcome these disadvantages,this paper proposes a modified FA based on elite neighborhood guide.The algorithm leads fireflies to move by using the information of elite neighborhoods,so it can reduce the occurrence of oscillation and accelerate convergence.Meanwhile,if there is no elite neighborhood around some fireflies,they will do opposition-based learning to help the algorithm to get out of the local optima by using their own information.The experimental results show that the proposed algorithm is superior to other algorithms.
作者:
汪春峰 褚新月
Wang Chunfeng;Chu Xinyue(College of Mathematics and Information Science,Henan Normal University,Xinxiang 453007,China)
机构地区:
betway官方app 数学与信息科学学院
出处:
《betway官方app 学报:自然科学版》 CAS 北大核心 2019年第6期15-21,共7页
基金:
国家自然科学基金(11671122) 河南省高等学校重点科研项目(19A110021) betway官方app 个人科研项目结余经费资助专项(校20180562)
关键词:
萤火虫算法 精英邻居 反向学习 时间计算复杂度
firefly algorithm elite neighborhood opposition-based learning time complexity
分类号:
O221 [理学—运筹学与控制论]