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

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 historic...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 19; H. 11; S. 3083 - 3107
Hauptverfasser: Liu, Ruochen, Niu, Xu, Fan, Jing, Mu, Caihong, Jiao, Licheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2015
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Computational Intelligence
Control
Decomposition
Design techniques
Engineering
Evolutionary algorithms
Genetic algorithms
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Multiple objective analysis
Mutation
Optimization algorithms
Optimization techniques
Pareto optimization
Performance evaluation
Prediction models
Robotics
Multi-objective optimization
Decomposition
Forecasting model
Dynamic environment
ISSN:1432-7643, 1433-7479
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-014-1470-y