M. Dominguez, Sergio Nesmachnow, José-Isidro Hernández-Vega
{"title":"Planning a drone fleet using artificial intelligence for search and rescue missions","authors":"M. Dominguez, Sergio Nesmachnow, José-Isidro Hernández-Vega","doi":"10.1109/INTERCON.2017.8079646","DOIUrl":null,"url":null,"abstract":"This article presents the simulation genetic algorithms with multi-agent system application to solve problems of collaboration and coordination, with the goal of minimizing the travel time of a fleet of drones. The experimental analysis compares the travel time of a deterministic algorithm versus a probabilistic algorithm in a multi-agent system with BDI architecture. The results show that the genetic algorithm delivers significant improvements in travel time, exceeding the deterministic algorithm by up to 35% on average. This article also discusses the feasibility of combining genetic algorithms and multi-agent systems for solving people search and rescue problems.","PeriodicalId":229086,"journal":{"name":"2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INTERCON.2017.8079646","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
This article presents the simulation genetic algorithms with multi-agent system application to solve problems of collaboration and coordination, with the goal of minimizing the travel time of a fleet of drones. The experimental analysis compares the travel time of a deterministic algorithm versus a probabilistic algorithm in a multi-agent system with BDI architecture. The results show that the genetic algorithm delivers significant improvements in travel time, exceeding the deterministic algorithm by up to 35% on average. This article also discusses the feasibility of combining genetic algorithms and multi-agent systems for solving people search and rescue problems.
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