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A swarm-based artificial immune system for solving multimodal functions

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, gene...

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Bibliographic Details
Main Authors: Yap D.F.W., Koh S.P., Tiong S.K., Prajindra S.K.
Other Authors: 22952562500
Format: Article
Published: 2023
Subjects:
Approximation theory
Convergence of numerical methods
Functions
Genetic algorithms
Immunology
Artificial Immune System
Convergence rates
Engineering problems
Global searching ability
Mathematical functions
Meta heuristic algorithm
Multi modal function
Optimization problems
Rate of convergence
Particle swarm optimization (PSO)
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Summary: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.

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