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|dc.description.abstract||Artificial Immune Systems (AIS) have attracted enormous attention among researchers because the algorithms are able to improve global searching ability and efficiency. Nevertheless, the rate of convergence for AIS is relatively slow compared to other metaheuristic algorithms. On the other hand, genetic algorithms (GAs) and particle swarm optimization (PSO) have been used successfully in solving optimization problems, although they tend to converge prematurely. Therefore, the good attributes of AIS and PSO are merged in order to reduce this limitation. It is observed that the proposed hybrid AIS (HAIS) achieved better performances in terms of convergence rate, accuracy, and stability against GA and AIS by comparing the optimization results of the mathematical functions. A similar result was achieved by HAIS in the engineering problem when compared to GA, PSO, and AIS. Copyright © 2011 Taylor & Francis Group, LLC.||en_US|
|dc.relation.ispartof||Applied Artificial Intelligence Volume 25, Issue 8, September 2011, Pages 693-707||en_US|
|dc.title||A swarm-based artificial immune system for solving multimodal functions||en_US|
|Appears in Collections:||COE Scholarly Publication|
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