Ensemble Many-Objective Optimization Algorithm Based on Voting Mechanism
| Autor: | Guohua Wu, Witold Pedrycz, Jianghan Zhu, Ponnuthurai Nagaratnam Suganthan, Huangke Chen, Wenbo Qiu |
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| Rok vydání: | 2022 |
| Předmět: |
Mathematical optimization
Optimization problem Computer science Mechanism (biology) Process (engineering) media_common.quotation_subject 05 social sciences Evolutionary algorithm Sorting 050301 education 02 engineering and technology Computer Science Applications Human-Computer Interaction Control and Systems Engineering Voting 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Electrical and Electronic Engineering 0503 education Software media_common |
| Zdroj: | IEEE Transactions on Systems, Man, and Cybernetics: Systems. 52:1716-1730 |
| ISSN: | 2168-2232 2168-2216 |
| Popis: | Sorting solutions play a key role in using evolutionary algorithms (EAs) to solve many-objective optimization problems (MaOPs). Generally, different solution-sorting methods possess different advantages in dealing with distinct MaOPs. Focusing on this characteristic, this article proposes a general voting-mechanism-based ensemble framework (VMEF), where different solution-sorting methods can be integrated and work cooperatively to select promising solutions in a more robust manner. In addition, a strategy is designed to calculate the contribution of each solution-sorting method and then the total votes are adaptively allocated to different solution-sorting methods according to their contribution. Solution-sorting methods that make more contribution to the optimization process are rewarded with more votes and the solution-sorting methods with poor contribution will be punished in a period of time, which offers a good feedback to the optimization process. Finally, to test the performance of VMEF, extensive experiments are conducted in which VMEF is compared with five state-of-the-art peer many-objective EAs, including NSGA-III, SPEA/R, hpaEA, BiGE, and grid-based evolutionary algorithm. Experimental results demonstrate that the overall performance of VMEF is significantly better than that of these comparative algorithms. |
| Databáze: | OpenAIRE |
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