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An improved artificial immune system based on antibody remainder 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 hypermutation in CSA itself cannot alwa...

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Bibliographic Details
Main Authors: Yap D.F.W., Habibullah A., Koh S.P., Tiong S.K.
Other Authors: 22952562500
Format: Conference paper
Published: 2023
Subjects:
Affinity maturation
Antibody
Antigen
Clonal selection
Component
Mutation
Algorithms
Antibodies
Antigens
Engineering research
Functions
Immunology
Innovation
Artificial Immune System
Clonal selection algorithms
Complex optimization
Global searching ability
Hyper mutation
Mathematical functions
Nature-inspired algorithms
Optimization problems
Single objective
Particle swarm optimization (PSO)
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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 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.

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