A Dynamic Multiobjective Evolutionary Algorithm Based on Decision Variable Classification

In recent years, dynamic multiobjective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multiobjective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multiobjec...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 52; H. 3; S. 1602 - 1615
Hauptverfasser: Liang, Zhengping, Wu, Tiancheng, Ma, Xiaoliang, Zhu, Zexuan, Yang, Shengxiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Benchmark testing
Classification
Convergence
Decision variable classification
dynamic multiobjective evolutionary algorithm (DMOEA)
dynamic multiobjective optimization problem (DMOP)
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Heuristic algorithms
multiobjective evolutionary algorithm
multiobjective optimization problem (MOP)
Multiple objective analysis
Optimization
Sociology
Statistics
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:In recent years, dynamic multiobjective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multiobjective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multiobjective evolutionary algorithms. Maintaining a good balance of population diversity and convergence is critical to the performance of DMOEAs. To address the above issue, a DMOEA based on decision variable classification (DMOEA-DVC) is proposed in this article. DMOEA-DVC divides the decision variables into two and three different groups in static optimization and changes response stages, respectively. In static optimization, two different crossover operators are used for the two decision variable groups to accelerate the convergence while maintaining good diversity. In change response, DMOEA-DVC reinitializes the three decision variable groups by maintenance, prediction, and diversity introduction strategies, respectively. DMOEA-DVC is compared with the other six state-of-the-art DMOEAs on 33 benchmark DMOPs. The experimental results demonstrate that the overall performance of the DMOEA-DVC is superior or comparable to that of the compared algorithms.
Bibliographie:ObjectType-Article-1
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2020.2986600