An Improved Artificial Immune System Based On Antibody Reminder Method For Mathematical Function Optimization
Artificial immune system (AIS) is one of the nature inspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be improved further because the hyper mutation in CSA itself cannot alw...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/20210/1/AN%20IMPROVED%20ARTIFICIAL%20IMMUNE%20SYSTEM%20BASED%20ON%20ANTIBODY%20REMINDER%20METHOD%20FOR%20MATHEMATICAL%20FUNCTION%20OPTIMIZATION-DAVID%20F%20W%20YAP-MAK%2000509%20RAF.pdf http://eprints.utem.edu.my/id/eprint/20210/ |
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| Summary: | Artificial immune system (AIS) is one of the nature inspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be improved further because the hyper mutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAS) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. I n this study, the CSA is modified using the best solutions for each exposure (iteration) namely Remainder-CSA. The results show that the proposed algorithm is able to improve the conventional CSA in terms of accuracy and stability for single objective functions. |
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