Artificial immune system based remainder method for multimodal mathematical function optimization
Artificial immune system (AIS) is one of the nature-inspired algorithm for solving optimization problems. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability compare to other meta-heuristic methods. However, the CSA rate of convergence and accuracy can be further imp...
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my.uniten.dspace-295812023-12-28T15:05:42Z Artificial immune system based remainder method for multimodal mathematical function optimization Yap D.F.W. Koh S.P. Tiong S.K. 22952562500 22951210700 15128307800 Affinity maturation Antibody Antigen Clonal selection Component Mutation Artificial immune system (AIS) is one of the nature-inspired algorithm for solving optimization problems. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability compare to other meta-heuristic methods. However, the CSA rate of convergence and accuracy can be further improved as the hyper mutation in CSA itself cannot always guarantee a better solution. Conversely, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have an inclination to converge prematurely. In this work, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. Simulation results show that the proposed algorithm is able to enhance the performance of the conventional CSA in terms of accuracy and stability for single objective functions. � IDOSI Publications, 2011. Final 2023-12-28T07:05:42Z 2023-12-28T07:05:42Z 2011 Article 2-s2.0-84856165143 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856165143&partnerID=40&md5=5481147bbe109b2dbe97fdbae48307a3 https://irepository.uniten.edu.my/handle/123456789/29581 14 10 1507 1514 Scopus |
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Affinity maturation Antibody Antigen Clonal selection Component Mutation |
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Affinity maturation Antibody Antigen Clonal selection Component Mutation Yap D.F.W. Koh S.P. Tiong S.K. Artificial immune system based remainder method for multimodal mathematical function optimization |
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Artificial immune system (AIS) is one of the nature-inspired algorithm for solving optimization problems. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability compare to other meta-heuristic methods. However, the CSA rate of convergence and accuracy can be further improved as the hyper mutation in CSA itself cannot always guarantee a better solution. Conversely, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have an inclination to converge prematurely. In this work, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. Simulation results show that the proposed algorithm is able to enhance the performance of the conventional CSA in terms of accuracy and stability for single objective functions. � IDOSI Publications, 2011. |
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22952562500 |
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22952562500 Yap D.F.W. Koh S.P. Tiong S.K. |
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Article |
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Yap D.F.W. Koh S.P. Tiong S.K. |
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Yap D.F.W. |
title |
Artificial immune system based remainder method for multimodal mathematical function optimization |
title_short |
Artificial immune system based remainder method for multimodal mathematical function optimization |
title_full |
Artificial immune system based remainder method for multimodal mathematical function optimization |
title_fullStr |
Artificial immune system based remainder method for multimodal mathematical function optimization |
title_full_unstemmed |
Artificial immune system based remainder method for multimodal mathematical function optimization |
title_sort |
artificial immune system based remainder method for multimodal mathematical function optimization |
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2023 |
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1806424080005464064 |