Multi-Swarm bat algorithm
In this study a new Bat Algorithm (BA) based on multi-swarm technique called the Multi-Swarm Bat Algorithm (MSBA) is proposed to address the problem of premature convergence phenomenon. The problem happens when search process converges to non-optimal solution due to the loss of diversity during the...
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2023
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my.uniten.dspace-225202023-05-29T14:01:35Z Multi-Swarm bat algorithm Taha A.M. Chen S.-D. Mustapha A. 55699699200 7410253413 57200530694 In this study a new Bat Algorithm (BA) based on multi-swarm technique called the Multi-Swarm Bat Algorithm (MSBA) is proposed to address the problem of premature convergence phenomenon. The problem happens when search process converges to non-optimal solution due to the loss of diversity during the evolution process. MSBA was designed with improved ability in exploring new solutions, which was essential in reducing premature convergence. The exploration ability was improved by having a number of sub-swarms watching over the best local optima. In MSBA, when the quality of best local optima does not improve after a pre-defined number of iterations, the population is split equally into several smaller sub-swarms, with one of them remains close to the current best local optima for further exploitation while the other sub-swarms continue to explore for new local optima. The proposed algorithm has been applied in feature selection problem and the results were compared against eight algorithms, which are Ant Colony Optimization (ACO), Genetic Algorithm (GA), Tabu Search (TS), Scatter Search (SS), Great Deluge Algorithm (GDA) and stander BA. The results showed that the MSBA is much more effective that it is able to find new best solutions at times when the rest of other algorithms are not able to. � Maxwell Scientific Organization, 2015. Final 2023-05-29T06:01:34Z 2023-05-29T06:01:34Z 2015 Article 10.19026/rjaset.10.1839 2-s2.0-84942038008 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84942038008&doi=10.19026%2frjaset.10.1839&partnerID=40&md5=f3f5f33089ca0262b5a7f73ba5eb9d2b https://irepository.uniten.edu.my/handle/123456789/22520 10 12 1389 1395 All Open Access, Gold, Green Maxwell Science Publications Scopus |
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In this study a new Bat Algorithm (BA) based on multi-swarm technique called the Multi-Swarm Bat Algorithm (MSBA) is proposed to address the problem of premature convergence phenomenon. The problem happens when search process converges to non-optimal solution due to the loss of diversity during the evolution process. MSBA was designed with improved ability in exploring new solutions, which was essential in reducing premature convergence. The exploration ability was improved by having a number of sub-swarms watching over the best local optima. In MSBA, when the quality of best local optima does not improve after a pre-defined number of iterations, the population is split equally into several smaller sub-swarms, with one of them remains close to the current best local optima for further exploitation while the other sub-swarms continue to explore for new local optima. The proposed algorithm has been applied in feature selection problem and the results were compared against eight algorithms, which are Ant Colony Optimization (ACO), Genetic Algorithm (GA), Tabu Search (TS), Scatter Search (SS), Great Deluge Algorithm (GDA) and stander BA. The results showed that the MSBA is much more effective that it is able to find new best solutions at times when the rest of other algorithms are not able to. � Maxwell Scientific Organization, 2015. |
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55699699200 |
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55699699200 Taha A.M. Chen S.-D. Mustapha A. |
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Article |
| author |
Taha A.M. Chen S.-D. Mustapha A. |
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Taha A.M. Chen S.-D. Mustapha A. Multi-Swarm bat algorithm |
| author_sort |
Taha A.M. |
| title |
Multi-Swarm bat algorithm |
| title_short |
Multi-Swarm bat algorithm |
| title_full |
Multi-Swarm bat algorithm |
| title_fullStr |
Multi-Swarm bat algorithm |
| title_full_unstemmed |
Multi-Swarm bat algorithm |
| title_sort |
multi-swarm bat algorithm |
| publisher |
Maxwell Science Publications |
| publishDate |
2023 |
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1806423457241497600 |
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13.211869 |