期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 51, 期 9, 页码 5652-5663出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2019.2957324
关键词
Optimization; Sociology; Statistics; Convergence; Nonlinear equations; Transforms; Maintenance engineering; Multiobjective optimization technique; niching technique; nonlinear equation systems (NESs); population diversity
资金
- National Nature Science Foundation of China [61772391, 61402534, 61876163]
- Natural Science Basic Research Plan in Shaanxi Province of China [2018JQ6051, 2017JQ6059]
- Hong Kong Special Administrative Region, China
- France National Research Agency [A-CityU101/16]
- Young Talent Fund of University Association for Science and Technology in Shaanxi, China
- Hong Kong Scholars Program
A two-phase evolutionary algorithm is proposed to find multiple solutions of a nonlinear equations system. The algorithm transforms the nonlinear equations system into a multimodal optimization problem by combining multiobjective optimization technique and niching technique in phase one, and using a detection method and a local search method in phase two to encourage convergence. Experimental results show that the proposed algorithm outperforms other state-of-the-art algorithms.
A two-phase evolutionary algorithm is developed to find multiple solutions of a nonlinear equations system. It transforms a nonlinear equations system into a multimodal optimization problem. In phase one of the proposed algorithm, a strategy combines a multiobjective optimization technique and a niching technique to maintain the population diversity. Phase two consists of a detection method and a local search method for encouraging the convergence. The detection method finds several promising subregions and the local search method locates the corresponding optimal solutions in each promising subregion. The experiments on a set of 30 nonlinear equation systems demonstrate that the proposed algorithm is better than other state-of-the-art algorithms.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据