Journal
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume 52, Issue 3, Pages 1716-1730Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2020.3034180
Keywords
Optimization; Sorting; Sociology; Convergence; Heuristic algorithms; Evolutionary computation; Transportation; Ensemble framework; evolutionary optimization; many-objective optimization; solution-sorting methods; voting
Funding
- National Natural Science Foundation of China [62073341]
- Natural Science Fund for Distinguished Young Scholars of Hunan Province [2019JJ20026]
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The research proposes a general voting-mechanism-based ensemble framework (VMEF) that integrates and cooperates different solution-sorting methods to achieve more robust solution selection. The framework employs a strategy to adaptively allocate total votes based on the contribution of each solution-sorting method, providing good feedback for the optimization process.
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.
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