A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model

Dynamic multi-objective optimization problem (DMOP) is quite challenging and it dues to that there are multiple conflicting objects changing over with time or environment. In this paper, a novel cooperative coevolutionary dynamic multi-objective optimization algorithm (PNSCCDMO) is proposed. The mai...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 18; H. 10; S. 1913 - 1929
Hauptverfasser: Liu, Ruochen, Chen, Yangyang, Ma, Wenping, Mu, Caihong, Jiao, Licheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2014
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Capital budgeting
Computational Intelligence
Control
Decomposition
Engineering
Genetic algorithms
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Multiple objective analysis
Optimization algorithms
Optimization techniques
Pareto optimization
Prediction models
Regression analysis
Robotics
Evolutionary algorithms
Dynamic multi-objective optimization
Coevolution
Predictive model
ISSN:1432-7643, 1433-7479
Online-Zugang:Volltext
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Zusammenfassung:Dynamic multi-objective optimization problem (DMOP) is quite challenging and it dues to that there are multiple conflicting objects changing over with time or environment. In this paper, a novel cooperative coevolutionary dynamic multi-objective optimization algorithm (PNSCCDMO) is proposed. The main idea of a new cooperative coevolution based on non-dominated sorting is that it allows the decomposition process of the optimization problem according to the search space of decision variables, and each species subcomponents will cooperate to evolve for better solutions. This way derives from nature and can improve convergence significantly. A modified linear regression prediction strategy is used to make rapid response to the new changes in the environment. The effectiveness of PNSCCDMO is validated against various of DMOPs compared with the other four algorithms, and the experimental result indicates PNSCCDMO has a good capability to track the Pareto front as it is changed with time in dynamic environments.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-013-1175-7