Hybrid Ant Colony Optimization Algorithm for Multiple Knapsack Problem

S. Fidanova
{"title":"Hybrid Ant Colony Optimization Algorithm for Multiple Knapsack Problem","authors":"S. Fidanova","doi":"10.1109/ICRAIE51050.2020.9358351","DOIUrl":null,"url":null,"abstract":"0pt0pt Multiple Knapsack Problem (MKP) is a difficult combinatorial optimization problem. It is one of the most studied optimization problem, because a lot of economical, practical and industrial problems can be described as knapsack problem. MKP is a NP-hard problem and requires the use of a large amount of computer resources if traditional numerical method is applied. Therefore methaeuristic methods are more suitable for such complex problems. We apply Ant Colony Optimization (ACO), because it is one of the best metaheuristic methods, prepared for solving combinatorial optimization problems. it is an approximate method, that finds close to optimal solutions. In this paper we propose local search procedure, which we combine with a main ACO algorithm. The aim is improvement of the algorithm performance and achievement of better solutions.","PeriodicalId":149717,"journal":{"name":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRAIE51050.2020.9358351","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

0pt0pt Multiple Knapsack Problem (MKP) is a difficult combinatorial optimization problem. It is one of the most studied optimization problem, because a lot of economical, practical and industrial problems can be described as knapsack problem. MKP is a NP-hard problem and requires the use of a large amount of computer resources if traditional numerical method is applied. Therefore methaeuristic methods are more suitable for such complex problems. We apply Ant Colony Optimization (ACO), because it is one of the best metaheuristic methods, prepared for solving combinatorial optimization problems. it is an approximate method, that finds close to optimal solutions. In this paper we propose local search procedure, which we combine with a main ACO algorithm. The aim is improvement of the algorithm performance and achievement of better solutions.
多背包问题的混合蚁群优化算法
多背包问题(Multiple backpack Problem, MKP)是一个复杂的组合优化问题。由于许多经济的、实用的和工业的问题都可以被描述为背包问题,它是研究最多的优化问题之一。MKP是一个NP-hard问题,如果采用传统的数值方法,需要使用大量的计算机资源。因此,方法更适合于此类复杂的问题。我们采用蚁群优化算法,因为它是解决组合优化问题的最好的元启发式方法之一。它是一种近似方法,可以找到接近最优解。本文提出了一种局部搜索算法,并将其与一种主要的蚁群算法相结合。其目的是提高算法性能并获得更好的解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信