.png){kind=icon}
返回
Water cycle algorithm for solving constrained multi-objective optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2014.10.042.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a metaheuristic optimizer, the multi-objective water cycle algorithm (MOWCA), is presented for solving constrained multi-objective problems. The MOWCA is based on emulation of the water cycle process in nature. In this study, a set of non-dominated solutions obtained by the proposed algorithm is kept in an archive to be used to display the exploratory capability of the MOWCA as compared to other efficient methods in the literature. Moreover, to make a comprehensive assessment about the robustness and efficiency of the proposed algorithm, the obtained optimization results are also compared with other widely used optimizers for constrained and engineering design problems. The comparisons are carried out using tabular, descriptive, and graphical presentations. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Multi-objective optimization
Water cycle algorithm
Pareto optimal solutions
Benchmark function
Metaheuristics
Constrained optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
PREAI
分享收藏Multi-Gigabit Operation of a Compact, Broadband Modulator Based on ENZ Confinement in Indium Oxide
PREAI
分享收藏
A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type考虑发电机约束和多燃料类型的新混合优化算法的多目标最优潮流新方法.png)
ENERGY
ENERGYIF9.4
PREAI
分享收藏
查看更多内容