Huanlai Xing, Fuhong Song, Zhaoyuan Wang, Tianrui Li, Yan Yang
{"title":"最小化网络编码资源:一种改进的粒子群优化方法","authors":"Huanlai Xing, Fuhong Song, Zhaoyuan Wang, Tianrui Li, Yan Yang","doi":"10.1109/MSN.2016.060","DOIUrl":null,"url":null,"abstract":"This paper studies the problem of how to efficiently minimize network coding resource. A modified particle swarm optimization (PSO) algorithm is proposed to tackle the problem, with the concept of path-relinking (PR) integrated into the evolutionary framework. As an efficient local search heuristic that makes use of problem-specific domain knowledge, PR helps strike a better balance between global exploration and local exploitation for the evolutionary search. Simulation results demonstrate that the proposed algorithm overweighs a number of existing and commonly used evolutionary algorithms (EAs) in terms of the solution quality, convergence, and computational time.","PeriodicalId":135328,"journal":{"name":"2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)","volume":"201 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"On Minimizing Network Coding Resource: A Modified Particle Swarm Optimization Approach\",\"authors\":\"Huanlai Xing, Fuhong Song, Zhaoyuan Wang, Tianrui Li, Yan Yang\",\"doi\":\"10.1109/MSN.2016.060\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper studies the problem of how to efficiently minimize network coding resource. A modified particle swarm optimization (PSO) algorithm is proposed to tackle the problem, with the concept of path-relinking (PR) integrated into the evolutionary framework. As an efficient local search heuristic that makes use of problem-specific domain knowledge, PR helps strike a better balance between global exploration and local exploitation for the evolutionary search. Simulation results demonstrate that the proposed algorithm overweighs a number of existing and commonly used evolutionary algorithms (EAs) in terms of the solution quality, convergence, and computational time.\",\"PeriodicalId\":135328,\"journal\":{\"name\":\"2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)\",\"volume\":\"201 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MSN.2016.060\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MSN.2016.060","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On Minimizing Network Coding Resource: A Modified Particle Swarm Optimization Approach
This paper studies the problem of how to efficiently minimize network coding resource. A modified particle swarm optimization (PSO) algorithm is proposed to tackle the problem, with the concept of path-relinking (PR) integrated into the evolutionary framework. As an efficient local search heuristic that makes use of problem-specific domain knowledge, PR helps strike a better balance between global exploration and local exploitation for the evolutionary search. Simulation results demonstrate that the proposed algorithm overweighs a number of existing and commonly used evolutionary algorithms (EAs) in terms of the solution quality, convergence, and computational time.