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Soft Computing

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01.11.2015 | Methodologies and Application

An orthogonal predictive model-based dynamic multi-objective optimization algorithm

verfasst von: Ruochen Liu, Xu Niu, Jing Fan, Caihong Mu, Licheng Jiao

Erschienen in: Soft Computing | Ausgabe 11/2015

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Abstract

In this paper, a new dynamic multi-objective optimization evolutionary algorithm is proposed for tracking the Pareto-optimal set of time-changing multi-objective optimization problems effectively. In the proposed algorithm, to select individuals which are best suited for a new time from the historical optimal sets, an orthogonal predictive model is presented to predict the new individuals after the environment change is detected. Also, to converge to optimal front more quickly, an modified multi-objective optimization evolutionary algorithm based on decomposition is adopted. The proposed method has been extensively compared with other three dynamic multi-objective evolutionary algorithms over several benchmark dynamic multi-objective optimization problems. The experimental results indicate that the proposed algorithm achieves competitive results.

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Titel: An orthogonal predictive model-based dynamic multi-objective optimization algorithm
verfasst von: Ruochen Liu
Xu Niu
Jing Fan
Caihong Mu
Licheng Jiao
Publikationsdatum: 01.11.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 11/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1470-y

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