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Erschienen in: Natural Computing 3/2014

01.09.2014

Immune clonal coevolutionary algorithm for dynamic multiobjective optimization

verfasst von: Ronghua Shang, Licheng Jiao, Yujing Ren, Jia Wang, Yangyang Li

Erschienen in: Natural Computing | Ausgabe 3/2014

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Abstract

In this paper, a new evolutionary algorithm, called immune clonal coevolutionary algorithm (ICCoA) for dynamic multiobjective optimization (DMO) is proposed. On the basis of the basic principles of artificial immune system, the proposed algorithm adopts the immune clonal selection to solve DMO problems. In addition, the theory of coevolution is incorporated in ICCoA in global operation to preserve the diversity of Pareto-fronts. Moreover, coevolutionary competitive and cooperative operation is designed to enhance the uniformity and the diversity of the solutions. In comparison with NSGA-II, immune clonal algorithm for DMO and direction-based method, the simulation results obtained on 5 difficult test problems and on related performance metrics suggest that ICCoA can achieve better distributed solutions and be very effective in maintaining the uniformity of Pareto-fronts.

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Metadaten
Titel
Immune clonal coevolutionary algorithm for dynamic multiobjective optimization
verfasst von
Ronghua Shang
Licheng Jiao
Yujing Ren
Jia Wang
Yangyang Li
Publikationsdatum
01.09.2014
Verlag
Springer Netherlands
Erschienen in
Natural Computing / Ausgabe 3/2014
Print ISSN: 1567-7818
Elektronische ISSN: 1572-9796
DOI
https://doi.org/10.1007/s11047-014-9415-z

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