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Soft Computing

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01.04.2014 | Methodologies and Application

Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization

verfasst von: Ronghua Shang, Licheng Jiao, Yujing Ren, Lin Li, Luping Wang

Erschienen in: Soft Computing | Ausgabe 4/2014

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Abstract

The existing algorithms to solve dynamic multiobjective optimization (DMO) problems generally have difficulties in non-uniformity, local optimality and non-convergence. Based on artificial immune system, quantum evolutionary computing and the strategy of co-evolution, a quantum immune clonal coevolutionary algorithm (QICCA) is proposed to solve DMO problems. The algorithm adopts entire cloning and evolves the theory of quantum to design a quantum updating operation, which improves the searching ability of the algorithm. Moreover, coevolutionary strategy is incorporated in global operation and coevolutionary competitive operation and coevolutionary cooperative operation are designed to improve the uniformity, the diversity and the convergence performance of the solutions. The results on test problems and performance metrics compared with ICADMO and DBM suggest that QICCA has obvious effectiveness and advantages which shows great capability of evolving convergent, diverse and uniformly distributed Pareto fronts.

Vorheriger Artikel The bideterminants of matrices over semirings

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Titel: Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization
verfasst von: Ronghua Shang
Licheng Jiao
Yujing Ren
Lin Li
Luping Wang
Publikationsdatum: 01.04.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 4/2014
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-013-1085-8

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