.png){kind=icon}
返回
Labeling algorithms for multiple objective integer knapsack problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cor.2009.06.026.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The paper presents a generic labeling algorithm for finding all nondominated outcomes of the multiple objective integer knapsack problem (MOIKP). The algorithm is based on solving the multiple objective shortest path problem on an underlying network. Algorithms for constructing four network models, all representing the MOIKP, are also presented. Each network is composed of layers and each network algorithm, working forward layer by layer, identifies the set of all permanent nondominated labels for each layer. The effectiveness of the algorithms is supported with numerical results obtained for randomly generated problems for up to seven objectives while exact algorithms reported in the literature solve the multiple objective binary knapsack problem with up to three objectives. Extensions of the approach to other classes of problems including binary variables, bounded variables. multiple constraints, and time-dependent objective functions are possible. Published by Elsevier Ltd.
Keyword:
Multiple objective integer knapsack problem
Labeling algorithms
Shortest path 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
C
IF:
4.3
论文数:
6.5K
被引数:
2.1W
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
PREAI
分享收藏Floquet Majorana Fermions for Topological Qubits in Superconducting Devices and Cold-Atom Systems
OAAI
OAAI分享收藏On the computational efficiency of multiple objective metaheuristics. The knapsack problem case study关于多目标元启发式算法的计算效率。背包问题案例研究
PREAI
分享收藏Integrating partial optimization with scatter search for solving bi-criteria {0,1}-knapsack problems
PREAI
分享收藏
查看更多内容