计算机科学
渡线
算法
杂草
数学优化
趋同(经济学)
理论(学习稳定性)
人口
异步通信
人工智能
机器学习
数学
计算机网络
经济增长
生物
农学
社会学
人口学
经济
作者
Fuqing Zhao,Songlin Du,Hao Lu,Weimin Ma,Houbin Song
标识
DOI:10.1080/09540091.2021.1917517
摘要
The invasive weed algorithm (IWO) is a meta-heuristic algorithm, which is an effective and promising optimiser to address the optimisation problems. In this study, a hybrid algorithm based on the self-adaptive invasive weed algorithm (IWO) and differential evolution algorithm (DE), named SIWODE, is proposed to address the continuous optimisation problems. In the proposed SIWODE, first, the two parameters are adaptively proposed to improve the convergence speed of the algorithm. Second, the crossover and mutation operations are introduced in SIWODE to improve the population diversity and increase the exploration capability during the iterative process. Furthermore, a local perturbation strategy is presented to improve exploitation ability during the late process. The exploration and exploitation ability of the algorithm is effectively balanced by cooperative mechanisms. The experiment results of SIWODE show that the SIWODE has the superior searching quality and stability than other mentioned approaches.
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