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An ensemble learning-based multi-population evolutionary framework for multi-scenario multi-objective optimization problems
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DOI：10.1016/j.knosys.2023.110708.png)
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Abstract
En 中文
Multi-scenario multi-objective optimization problems (MSMOPs) are a topic of considerable interest in the field of optimization. The MSMOP contains a set of multi-objective optimization problems arising from varying operating conditions, with the goal of determining a set of communal compromise solutions. However, there are few universal methods available for MSMOPs. This paper presents a general method incorporating transfer learning for MSMOPs. First, a multi-scenario ensemble framework that transfers knowledge between scenarios is developed to combine arbitrary multi-objective evolutionary algorithms, where a scenario-based comprehensive evaluation indicator is developed for combination with base learners. Then, an adaptive decomposition-based multi-objective evolutionary algorithm with a bi-layer selection (EADaBS) is proposed and embedded within the framework as a base learner. EADaBS incorporates an adaptive fitness assignment in its first layer to facilitate exploration, and density measurement in its second layer to ensure exploitation. A rebalancing operator is also designed to aid the population towards the Pareto front. Finally, a multitude of experiments is conducted to verify the effectiveness and efficiency of the proposed algorithms. Two three-scenario multiobjective optimization problems are designed and utilized as test problems. The experimental results clearly demonstrate that the proposed framework outperforms existing state-of-the-art algorithms. In conclusion, this research provides new insights into the solution of MSMOPs.& COPY; 2023 Elsevier B.V. All rights reserved.
Keywords:
Multi -scenario optimization problem
Evolutionary algorithm
Bi-compromise task
Ensemble learning
Bi-layer selection
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W