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Neural Computing and Applications

Erschienen in:

01.05.2015 | Original Article

A chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problems

verfasst von: A. Rezaee Jordehi

Erschienen in: Neural Computing and Applications | Ausgabe 4/2015

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Abstract

Artificial immune system algorithm (AIS) is a population-based global heuristic optimisation algorithm. It is inspired by immune system of human bodies. Alleviating premature convergence problem of heuristic optimisation algorithms is a hot research area. In this study, chaotic-based strategies are embedded into AIS to alleviate its premature convergence problem. Four various chaotic-based AIS strategies with five different chaotic map functions (totally 20 cases) are examined, and the best one is chosen as the best chaotic paradigm for AIS. The results of applying the proposed chaotic AIS to a variety of unimodal and multimodal benchmark functions reveal that it offers high-quality solutions. It significantly outperforms conventional AIS and gravitational search algorithm. The outperformance is both in terms of accuracy of solutions and stability in finding accurate solutions.

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Titel: A chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problems
verfasst von: A. Rezaee Jordehi
Publikationsdatum: 01.05.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 4/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1751-5

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