.png){kind=icon}
返回
A binary grey wolf optimizer for the multidimensional knapsack problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2019.105645.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Grey Wolf Optimizer (GWO) is a new meta-heuristic that mimics the leadership hierarchy and group hunting mechanism of grey wolves in nature. A binary version is developed to tackle the multidimensional knapsack problem which has an extensive engineering background. The proposed binary grey wolf optimizer integrates some important features including an initial elite population generator, a pseudo-utility-based quick repair operator, a new evolutionary mechanism with a differentiated position updating strategy. The proposed algorithm takes full advantage of the knowledge of the problem to be solved and highlights the distinctive feature of the optimizer in the family of evolutionary algorithm. Experimental results statistically show the effectiveness of the new optimizer and the superiority of the proposed algorithm in solving the multidimensional knapsack problem, especially the large-scale problem. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Optimization
Grey wolf optimizer
Multidimensional knapsack problem
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
.png){kind=icon}
分享收藏分享收藏分享收藏
查看更多内容