.png){kind=icon}
返回
Genetic local search for multi-objective combinatorial optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/S0377-2217(01)00104-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The paper presents a new genetic local search (GLS) algorithm for mufti-objective combinatorial optimization (MOCO). The goal of the algorithm is to generate in a short time a set of approximately efficient solutions that will allow the decision maker to choose a good compromise solution. In each iteration, the algorithm draws at random a utility function and constructs a temporary population composed of a number of best solutions among the prior generated solutions. Then, a pair of solutions selected at random from the temporary population is recombined. Local search procedure is applied to each offspring. Results of the presented experiment indicate that the algorithm outperforms other mufti-objective methods based on GLS and a Pareto ranking-based mufti-objective genetic algorithm (GA) on travelling salesperson problem (TSP). (C) 2002 Elsevier Science B.V. All rights reserved.
Keyword:
multi-objective combinatorial optimization
metaheuristics
genetic local search
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
论文数:
2.2W
被引数:
6.4W
机构
暂无机构信息
引用论文
Floquet Majorana Fermions for Topological Qubits in Superconducting Devices and Cold-Atom Systems
OAAI
OAAI分享收藏Injury-Dependent and Disability-Specific Lumbar Spinal Gene Regulation following Sciatic Nerve Injury in the Rat大鼠坐骨神经损伤后损伤依赖性和残疾特异性腰椎基因调控.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏
查看更多内容