{"title":"多背包问题的混合蚁群优化算法","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":"{\"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}","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}
Hybrid Ant Colony Optimization Algorithm for Multiple Knapsack Problem
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.