{"title":"无线传感器网络中聚类和路由的进化计算方法","authors":"P. Kuila, P. K. Jana","doi":"10.4018/978-1-5225-0058-2.CH011","DOIUrl":null,"url":null,"abstract":"With proliferation of Computational Intelligence (CI), evolutionary algorithms have drawn enormous attention among researchers. Such algorithms have been studied to solve many optimization problems. Clustering and routing are two well known optimization problems which are well researched in the field of Wireless Sensor Networks (WSNs). These problems are NP-hard. Therefore, many researchers have applied meta-heuristic approaches to develop various evolutionary algorithms to solve them. In this chapter, the authors rigorously study and present various evolutionary algorithms that include Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution, etc. and show how these algorithms are applied to solve clustering and routing problems in WSNs. The chapter starts with an introduction of WSNs along with clustering and routing problems in WSNs accompanied by a discussion why these problems are solved by evolutionary algorithms. The authors then give an overview of various evolutionary approaches that are applied to solve clustering and routing problems. Various evolutionary algorithms are then presented towards the solution of these problems. A comparison table is also made by highlighting strengths and weaknesses of the algorithms. Finally, the authors present new directions of future research in this domain.","PeriodicalId":68742,"journal":{"name":"传感技术(英文)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Evolutionary Computing Approaches for Clustering and Routing in Wireless Sensor Networks\",\"authors\":\"P. Kuila, P. K. Jana\",\"doi\":\"10.4018/978-1-5225-0058-2.CH011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With proliferation of Computational Intelligence (CI), evolutionary algorithms have drawn enormous attention among researchers. Such algorithms have been studied to solve many optimization problems. Clustering and routing are two well known optimization problems which are well researched in the field of Wireless Sensor Networks (WSNs). These problems are NP-hard. Therefore, many researchers have applied meta-heuristic approaches to develop various evolutionary algorithms to solve them. In this chapter, the authors rigorously study and present various evolutionary algorithms that include Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution, etc. and show how these algorithms are applied to solve clustering and routing problems in WSNs. The chapter starts with an introduction of WSNs along with clustering and routing problems in WSNs accompanied by a discussion why these problems are solved by evolutionary algorithms. The authors then give an overview of various evolutionary approaches that are applied to solve clustering and routing problems. Various evolutionary algorithms are then presented towards the solution of these problems. A comparison table is also made by highlighting strengths and weaknesses of the algorithms. Finally, the authors present new directions of future research in this domain.\",\"PeriodicalId\":68742,\"journal\":{\"name\":\"传感技术(英文)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"传感技术(英文)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/978-1-5225-0058-2.CH011\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"传感技术(英文)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-5225-0058-2.CH011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evolutionary Computing Approaches for Clustering and Routing in Wireless Sensor Networks
With proliferation of Computational Intelligence (CI), evolutionary algorithms have drawn enormous attention among researchers. Such algorithms have been studied to solve many optimization problems. Clustering and routing are two well known optimization problems which are well researched in the field of Wireless Sensor Networks (WSNs). These problems are NP-hard. Therefore, many researchers have applied meta-heuristic approaches to develop various evolutionary algorithms to solve them. In this chapter, the authors rigorously study and present various evolutionary algorithms that include Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution, etc. and show how these algorithms are applied to solve clustering and routing problems in WSNs. The chapter starts with an introduction of WSNs along with clustering and routing problems in WSNs accompanied by a discussion why these problems are solved by evolutionary algorithms. The authors then give an overview of various evolutionary approaches that are applied to solve clustering and routing problems. Various evolutionary algorithms are then presented towards the solution of these problems. A comparison table is also made by highlighting strengths and weaknesses of the algorithms. Finally, the authors present new directions of future research in this domain.