Yap, David F. W. and Habibullah, Akbar and Koh, S. P. and Tiong, S. K. (2010) An Improved Artificial Immune System Based On Antibody Reminder Method For Mathematical Function Optimization. In: 8th IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010 , 13-14 December 2010, Kuala Lumpur. (Submitted)
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AN IMPROVED ARTIFICIAL IMMUNE SYSTEM BASED ON ANTIBODY REMINDER METHOD FOR MATHEMATICAL FUNCTION OPTIMIZATION-DAVID F W YAP-MAK 00509 RAF.pdf Download (2MB) |
Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Artificial intelligence, Image processing, Immune system -- Computer simulation |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics |
| Divisions: | Faculty of Electronics and Computer Engineering |
| Depositing User: | Users 4097 not found. |
| Date Deposited: | 28 Dec 2017 08:13 |
| Last Modified: | 28 Dec 2017 08:13 |
| URI: | http://eprints.utem.edu.my/id/eprint/20210 |
| Statistic Details: | View Download Statistic |
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