An Experimentation System for Testing Bee Behavior Based Algorithm to Solving a Transportation Problem
Identifieur interne : 000801 ( Main/Exploration ); précédent : 000800; suivant : 000802An Experimentation System for Testing Bee Behavior Based Algorithm to Solving a Transportation Problem
Auteurs : Adam Kakol [Pologne] ; Iwona Pozniak-Koszalka [Pologne] ; Leszek Koszalka [Pologne] ; Andrzej Kasprzak [Pologne] ; J. Burnham [Royaume-Uni]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
Abstract: This paper describes a system, called eTransport to solve a transportation problem. The system was applied in experiments that test meta-heuristic algorithms for solving such optimization task. The engine of this system is based on a bee behavior based algorithm, called KAPI, designed by the authors. In order to make a comparison of the results obtained by KAPI, the Ants Colony System and Tabu Search algorithms were also applied. The eTransport simulator can generate real-life scenarios on Google Maps, while configuring and running tested algorithms, and finally, displaying the solutions found. Some illustrative examples of experiments are presented and discussed. The analysis of results of multi-scenario simulations shows the advantages of the KAPI algorithm, and justifies the conclusion that KAPI is much better and more effectiveat finding solutions to problems of this kind than other known algorithms.
Url:
DOI: 10.1007/978-3-642-20042-7_2
Affiliations:
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<front><div type="abstract" xml:lang="en">Abstract: This paper describes a system, called eTransport to solve a transportation problem. The system was applied in experiments that test meta-heuristic algorithms for solving such optimization task. The engine of this system is based on a bee behavior based algorithm, called KAPI, designed by the authors. In order to make a comparison of the results obtained by KAPI, the Ants Colony System and Tabu Search algorithms were also applied. The eTransport simulator can generate real-life scenarios on Google Maps, while configuring and running tested algorithms, and finally, displaying the solutions found. Some illustrative examples of experiments are presented and discussed. The analysis of results of multi-scenario simulations shows the advantages of the KAPI algorithm, and justifies the conclusion that KAPI is much better and more effectiveat finding solutions to problems of this kind than other known algorithms.</div>
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