Please use this identifier to cite or link to this item:
|Title:||An improved artificial immune system based on antibody remainder method for mathematical function optimization||Authors:||Yap, D.F.W.
|Issue Date:||2010||Journal:||Proceeding, 2010 IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010 2010, Article number 5703996, Pages 174-177||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 hypermutation 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. In 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. ©2010 IEEE.||DOI:||10.1109/SCORED.2010.5703996|
|Appears in Collections:||COE Scholarly Publication|
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.