{"title":"Swarm optimization approach to non-stationary physical field survey problem using a group of autonomous underwater vehicles","authors":"A. Tolstikhin, I. Bychkov","doi":"10.47350/iccs-de.2020.25","DOIUrl":null,"url":null,"abstract":"The paper considers the problem of searching for the source of a non-stationary physical eld. We assume that the use of swarm algorithms may be applicable in this case. A hybrid of the Whale Optimization Algorithm and Grey Wolf Optimizer is proposed in this paper. The algorithm has several advantages over its origins: a more precise solution of the optimization problem for low-dimensional functions and a higher convergence rate of the first\niterations. Two modications were made to adapt the algorithm to the requirements of the problem. The proposed algorithm is used as a basis for a control strategy for a group of autonomous underwater vehicles. As a result, in the vast number of cases, the group can find the source within the given number of search iterations.","PeriodicalId":210887,"journal":{"name":"International Workshop on Information, Computation, and Control Systems for Distributed Environments","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on Information, Computation, and Control Systems for Distributed Environments","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47350/iccs-de.2020.25","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The paper considers the problem of searching for the source of a non-stationary physical eld. We assume that the use of swarm algorithms may be applicable in this case. A hybrid of the Whale Optimization Algorithm and Grey Wolf Optimizer is proposed in this paper. The algorithm has several advantages over its origins: a more precise solution of the optimization problem for low-dimensional functions and a higher convergence rate of the first
iterations. Two modications were made to adapt the algorithm to the requirements of the problem. The proposed algorithm is used as a basis for a control strategy for a group of autonomous underwater vehicles. As a result, in the vast number of cases, the group can find the source within the given number of search iterations.