MAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman Problem

This paper proposes a Multi-Agent based Particle Swarm Optimization (PSO) Framework for the Traveling salesman problem (MAPSOFT). The framework is a deployment of the recently proposed intelligent multi-agent based PSO model by the authors. MAPSOFT is made up of groups of agents that interact with o...

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Autores principales:	Blamah Nachamada Vachaku, Oluyinka Aderemi Adewumi, Wajiga Gregory, Baha Yusuf Benson
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	multi-agent system neighborhood retrospective topology belief-desire-intention space/model 68t42 68t20 68t05 68t37 Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/1f25c6b48e32487192921e15de2a4e28
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id	oai:doaj.org-article:1f25c6b48e32487192921e15de2a4e28
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spelling	oai:doaj.org-article:1f25c6b48e32487192921e15de2a4e282021-12-05T14:10:51ZMAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman Problem2191-026X10.1515/jisys-2020-0042https://doaj.org/article/1f25c6b48e32487192921e15de2a4e282020-12-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0042https://doaj.org/toc/2191-026XThis paper proposes a Multi-Agent based Particle Swarm Optimization (PSO) Framework for the Traveling salesman problem (MAPSOFT). The framework is a deployment of the recently proposed intelligent multi-agent based PSO model by the authors. MAPSOFT is made up of groups of agents that interact with one another in a coordinated search effort within their environment and the solution space. A discrete version of the original multi-agent model is presented and applied to the Travelling Salesman Problem. Based on the simulation results obtained, it was observed that agents retrospectively decide on their next moves based on consistent better fitness values obtained from present and prospective neighborhoods, and by reflecting back to previous behaviors and sticking to historically better results. These overall attributes help enhance the conventional PSO by providing more intelligence and autonomy within the swarm and thus contributed to the emergence of good results for the studied problem.Blamah Nachamada VachakuOluyinka Aderemi AdewumiWajiga GregoryBaha Yusuf BensonDe Gruyterarticlemulti-agent systemneighborhoodretrospectivetopologybelief-desire-intentionspace/model68t4268t2068t0568t37ScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 413-428 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	multi-agent system neighborhood retrospective topology belief-desire-intention space/model 68t42 68t20 68t05 68t37 Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	multi-agent system neighborhood retrospective topology belief-desire-intention space/model 68t42 68t20 68t05 68t37 Science Q Electronic computers. Computer science QA75.5-76.95 Blamah Nachamada Vachaku Oluyinka Aderemi Adewumi Wajiga Gregory Baha Yusuf Benson MAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman Problem
description	This paper proposes a Multi-Agent based Particle Swarm Optimization (PSO) Framework for the Traveling salesman problem (MAPSOFT). The framework is a deployment of the recently proposed intelligent multi-agent based PSO model by the authors. MAPSOFT is made up of groups of agents that interact with one another in a coordinated search effort within their environment and the solution space. A discrete version of the original multi-agent model is presented and applied to the Travelling Salesman Problem. Based on the simulation results obtained, it was observed that agents retrospectively decide on their next moves based on consistent better fitness values obtained from present and prospective neighborhoods, and by reflecting back to previous behaviors and sticking to historically better results. These overall attributes help enhance the conventional PSO by providing more intelligence and autonomy within the swarm and thus contributed to the emergence of good results for the studied problem.
format	article
author	Blamah Nachamada Vachaku Oluyinka Aderemi Adewumi Wajiga Gregory Baha Yusuf Benson
author_facet	Blamah Nachamada Vachaku Oluyinka Aderemi Adewumi Wajiga Gregory Baha Yusuf Benson
author_sort	Blamah Nachamada Vachaku
title	MAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman Problem
title_short	MAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman Problem
title_full	MAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman Problem
title_fullStr	MAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman Problem
title_full_unstemmed	MAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman Problem
title_sort	mapsoft: a multi-agent based particle swarm optimization framework for travelling salesman problem
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/1f25c6b48e32487192921e15de2a4e28
work_keys_str_mv	AT blamahnachamadavachaku mapsoftamultiagentbasedparticleswarmoptimizationframeworkfortravellingsalesmanproblem AT oluyinkaaderemiadewumi mapsoftamultiagentbasedparticleswarmoptimizationframeworkfortravellingsalesmanproblem AT wajigagregory mapsoftamultiagentbasedparticleswarmoptimizationframeworkfortravellingsalesmanproblem AT bahayusufbenson mapsoftamultiagentbasedparticleswarmoptimizationframeworkfortravellingsalesmanproblem
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MAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman Problem

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