.png){kind=icon}
返回
.png){kind=icon}
.png){kind=icon}
摘要
En 中文
This paper introduces multi-directional local search, a metaheuristic for multi-objective optimization. We first motivate the method and present an algorithmic framework for it. We then apply it to several known multi-objective problems such as the multi-objective multi-dimensional knapsack problem, the bi-objective set packing problem and the bi-objective orienteering problem. Experimental results show that our method systematically provides solution sets of comparable quality with state-of-the-art methods applied to benchmark instances of these problems, within reasonable CPU effort. We conclude that the proposed algorithmic framework is a viable option,when solving multi-objective optimization problems. (C) 2012 Elsevier Ltd. All rights reserved.
Keyword:
Multi-objective optimization
Metaheuristics
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
C
IF:
4.3
论文数:
6.5K
被引数:
1.8W
机构
引用论文
.png){kind=icon}
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.png)
PREAI
.png){kind=icon}
分享.png){kind=icon}
收藏
分享收藏分享收藏
查看更多内容