Enhanced Water Cycle Algorithm with Active Learning and Return Strategy

Caihua Chen, Peng Wang, Huachao Dong, Xinjing Wang

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

In order to improve the performance of Water Cycle Algorithm (WCA), an alternative adaptation approach for enhancing the global searching ability is proposed. The proposed algorithm, named WCA-ALR, uses a new diversity enhancement approach to effectively improve the exploration capability of the WCA. The proposed approach consists of two major modifications: (1) an active selection method for choosing learning targets; (2) a promising position sifting and returning strategy. The benefits prove that actively selecting a learning target performs better than that of learning from a fixed one. A promising position sifting and returning strategy can also enhance the exploration ability. In order to verify the performance, numerical experiments on five basic benchmark problems are conducted. Then, a set of benchmark problems from the CEC2017 on 10 and 30 dimensions are used to prove the effectiveness of WCA-ALR. Experimental results affirm that the proposed approach can obtain better results, compared to the original WCA.

源语言英语
主期刊名2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
634-640
页数7
ISBN(电子版)9781728121536
DOI
出版状态已出版 - 6月 2019
活动2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, 新西兰
期限: 10 6月 201913 6月 2019

出版系列

姓名2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

会议

会议2019 IEEE Congress on Evolutionary Computation, CEC 2019
国家/地区新西兰
Wellington
时期10/06/1913/06/19

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