Publication: Artificial immune system based on hybrid and external memory for mathematical function optimization
Date
2011
Authors
Yap D.F.W.
Koh S.P.
Tiong S.K.
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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 further improved 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. Thus, a hybrid PSO-AIS and a new external memory CSA based scheme called EMCSA are proposed. In hybrid PSO-AIS, the good features of PSO and AIS are combined in order to reduce any limitation. Alternatively, EMCSA captures all the best antibodies into the memory in order to enhance global searching capability. In this preliminary study, the results show that the performance of hybrid PSO-AIS compares favourably with other algorithms while EMCSA produced moderate results in most of the simulations. � 2011 IEEE.
Description
Keywords
affinity maturation , antibody , antigen , clonal selection , mutation , Algorithms , Antibodies , Functions , Immunology , Information science , Affinity maturation , Artificial Immune System , Clonal selection , Clonal selection algorithms , Complex optimization , External memory , Global searching ability , Global searching capabilities , Hyper mutation , Mathematical functions , mutation , Nature-inspired algorithms , Optimization problems , Other algorithms , Particle swarm optimization (PSO)