A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

Dynamic interval multiobjective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms (EAs) that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multiobjective cooperative co-evolutionary optimization...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 24; no. 1; pp. 142 - 156
Main Authors: Gong, Dunwei, Xu, Biao, Zhang, Yong, Guo, Yinan, Yang, Shengxiang
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Cooperative co-evolutionary optimization
dynamic optimization
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Heuristic algorithms
interval similarity
Mopping
multiobjective optimization
Multiple objective analysis
Optimization
Pareto optimization
Probability distribution
Programming
response strategy
Robots
Similarity
Sociology
Strategy
Variables
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:Dynamic interval multiobjective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms (EAs) that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multiobjective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two subpopulations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances as well as a multiperiod portfolio selection problem and compared with five state-of-the-art EAs. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2019.2912204