当前位置: X-MOL 学术Appl. Intell. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A multi-population differential evolution with best-random mutation strategy for large-scale global optimization
Applied Intelligence ( IF 5.3 ) Pub Date : 2020-01-25 , DOI: 10.1007/s10489-019-01613-2
Yongjie Ma , Yulong Bai

Differential evolution (DE) is an efficient population-based search algorithm with good robustness, but it faces challenges in dealing with Large-Scale Global Optimization (LSGO). In this paper, we proposed an improved multi-population differential evolution with best-random mutation strategy (called mDE-brM). The population is divided into three sub-populations based on the fitness values, each sub-population uses different mutation strategies and control parameters, individuals share different mutation strategies and control parameters by migrating among sub-populations. A novel mutation strategy is proposed, which uses the best individual and a randomly selected individual to generate base vector. The performance of mDE-brM is evaluated on the CEC 2013 LSGO benchmark suite and compared with 5 state-of-the-art optimization techniques. The results show that, compared with other contestant algorithms, mDE-brM has a competitive performance and better efficiency in LSGO.



中文翻译:

具有最佳随机变异策略的多种群差异进化,用于大规模全局优化

差分进化(DE)是一种有效的基于种群的搜索算法,具有良好的鲁棒性,但在处理大规模全局优化(LSGO)时面临挑战。在本文中,我们提出了一种具有最佳随机变异策略(称为mDE-brM)的改进的多种群差异进化。根据适应度值将种群分为三个亚群,每个亚群使用不同的变异策略和控制参数,个体通过在亚群之间迁移来共享不同的变异策略和控制参数。提出了一种新颖的突变策略,该策略使用最佳个体和随机选择的个体来生成基础载体。mDE-brM的性能在CEC 2013 LSGO基准套件上进行了评估,并与5种最新的优化技术进行了比较。

更新日期:2020-04-20
down
wechat
bug