Palina Bartashevich, Luigi Grimaldi, Sanaz Mostaghim
{"title":"PSO-based Search mechanism in dynamic environments: Swarms in Vector Fields","authors":"Palina Bartashevich, Luigi Grimaldi, Sanaz Mostaghim","doi":"10.1109/CEC.2017.7969450","DOIUrl":null,"url":null,"abstract":"This paper presents the Vector Field Map PSO (VFM-PSO) as a collective search algorithm for aerial micro-robots in environments with unknown external dynamics (such as wind). The proposed method is based on a multi-swarm approach and allows to cope with unknown disturbances arising by the vector fields in which the positions and the movements of the particles are highly affected. VFM-PSO requires gathering the information regarding the vector fields and one of our goals is to investigate the amount of the required information for a successful search mechanism. The experiments show that VFM-PSO can reduce the drift and improves the performance of the PSO algorithm despite incomplete information (awareness) about the structure of considered vector fields.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Congress on Evolutionary Computation (CEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2017.7969450","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
This paper presents the Vector Field Map PSO (VFM-PSO) as a collective search algorithm for aerial micro-robots in environments with unknown external dynamics (such as wind). The proposed method is based on a multi-swarm approach and allows to cope with unknown disturbances arising by the vector fields in which the positions and the movements of the particles are highly affected. VFM-PSO requires gathering the information regarding the vector fields and one of our goals is to investigate the amount of the required information for a successful search mechanism. The experiments show that VFM-PSO can reduce the drift and improves the performance of the PSO algorithm despite incomplete information (awareness) about the structure of considered vector fields.