A dynamic multi-objective evolutionary algorithm based on decision variable classification

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Názov: A dynamic multi-objective evolutionary algorithm based on decision variable classification
Autori: Liang, Zhengping, Wu, Tiancheng, Ma, Xiaoliang, Zhu, Zexuan, Yang, Shengxiang
Informácie o vydavateľovi: IEEE
Rok vydania: 2020
Zbierka: De Montfort University, Leicester: Open Research Archive (DORA)
Predmety: Dynamic multi-objective optimization problem, multi-objective optimization problem, dynamic multi-objective evolutionary algorithm, multi-objective evolutionary algorithm, decision variable classification
Popis: The file attached to this record is the author's final peer reviewed version. ; In recent years, dynamic multi-objective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multi-objective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multi-objective evolutionary algorithms. Maintaining good balance of population diversity and convergence is critical to the performance of DMOEAs. To address the above issue, a dynamic multi-objective evolutionary algorithm based on decision variable classification (DMOEA-DVC) is proposed in this study. DMOEA-DVC divides the decision variables into two and three different groups in static optimization and change 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. Experimental results demonstrate that the overall performance of the DMOEA-DVC is superior or comparable to that of the compared algorithms.
Druh dokumentu: article in journal/newspaper
Popis súboru: application/pdf
Jazyk: English
Relation: https://dora.dmu.ac.uk/handle/2086/19485; https://doi.org/10.1109/tcyb.2020.2986600
DOI: 10.1109/tcyb.2020.2986600
Dostupnosť: https://dora.dmu.ac.uk/handle/2086/19485
https://doi.org/10.1109/tcyb.2020.2986600
Prístupové číslo: edsbas.8464214C
Databáza: BASE
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