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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2020-12-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2020-0042 |
| _version_ | 1811190829969047552 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T14:56:50Z |
| format | Article |
| id | doaj.art-1f25c6b48e32487192921e15de2a4e28 |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-04-11T14:56:50Z |
| publishDate | 2020-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-1f25c6b48e32487192921e15de2a4e282022-12-22T04:17:11ZengDe GruyterJournal of Intelligent Systems2191-026X2020-12-0130141342810.1515/jisys-2020-0042jisys-2020-0042MAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman ProblemBlamah Nachamada Vachaku0Oluyinka Aderemi Adewumi1Wajiga Gregory2Baha Yusuf Benson3Department of Computer Science, University of Jos, Jos, NigeriaSchool of Computer Science, University of KwaZulu-Natal, Durban, South AfricaDepartment of Computer Science, Moddibo Adama University of Technology, Yola, NigeriaDepartment of Information Technology, Moddibo Adama University of Technology, Yola, NigeriaThis 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.https://doi.org/10.1515/jisys-2020-0042multi-agent systemneighborhoodretrospectivetopologybelief-desire-intentionspace/model68t4268t2068t0568t37 |
| spellingShingle | Blamah Nachamada Vachaku Oluyinka Aderemi Adewumi Wajiga Gregory Baha Yusuf Benson MAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman Problem Journal of Intelligent Systems multi-agent system neighborhood retrospective topology belief-desire-intention space/model 68t42 68t20 68t05 68t37 |
| title | 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_short | 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 |
| topic | multi-agent system neighborhood retrospective topology belief-desire-intention space/model 68t42 68t20 68t05 68t37 |
| url | https://doi.org/10.1515/jisys-2020-0042 |
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