Multi-objective culture whale optimization algorithm for reservoir flood control operation

被引：0
|
作者：
Wang W. [1 ]
Dong J. [1 ]
Wang Z. [2 ]
Zuao Y. [3 ]
Zhang R. [4 ]
Li G. [1 ]
Hu M. [1 ]
机构：
[1] School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou
[2] School of Computer and Computational Sciences, Zhejiang University City College, Hangzhou
[3] Department of Mechanical Engineering, Zhejiang University City Collegej, Hangzhou
[4] Zhejiang Yugong Information Technology Co., Ltd., Hangzhou
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 11期
基金：
中国国家自然科学基金;
关键词：
culture algorithm; multi-objective optimization; Pareto optimality; reservoir flood control operation; whale optimization algorithm;
D O I：
10.13196/j.cims.2022.11.014
中图分类号：
学科分类号：
摘要：
Reservoir Flood Control Operation (RFCO) is a complex Multi-objective Problems (MOPs), which has many complex constraints, interdependent decision variables, and conflicting optimization objectives. Traditional research focuses on transforming multi-objective problem into single objective problem, which has some limitations in practical application. A Multi-objective Culture Whale Optimization Algorithm (MOCWOA) was presented for reservoir flood control operation. To improve the diversity and convergence accuracy of the results, the Cultural Algorithm (CA) was taken as MOCWOA's framework, the whale optimization algorithm was adopted in I he population space, and three knowledge structures in the belief space were defined. MOCWOA was first tested on benchmark problem. Then it was further applied to the actual reservoir flood control operation problem, and compared with several well-known multiobjective optimization algorithms. The results showed that MOCWOA algorithm had a certain competitive advantage. © 2022 CIMS. All rights reserved.
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页码：3494 / 3509
页数：15
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