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Knee-oriented expensive many-objective optimization via aggregation-dominance: A multi-task perspective
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AI
DOI：10.1016/j.swevo.2024.101813.png)
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Abstract
En 中文
Given the costs to implement whole Pareto optimal solutions, users often prefer solutions of interest, like knee points, which represent naturally preferred solutions without a specific bias. Recent surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) incorporating knee identification techniques have been suggested, but most of them cannot find knee solutions for expensive many-objective optimization problems. This work proposes a Kriging-assisted evolutionary multi-task algorithm with aggregation-dominance. The aggregation-dominance approach identifies knee points on an estimated Pareto front, from which subproblems are created and solved in parallel via Kriging-assisted multi-task optimization for guiding search knee solutions. Additionally, our proposed infill solutions selection strategy focuses on re-evaluating solutions converging in regions of interest. Experimental results on knee-oriented benchmark problems show that our algorithm outperforms state-of-the-art methods, with aggregation-dominance surpassing five existing knee identification techniques. We also validate the algorithm's performance on the portfolio allocation problem.
Keywords:
Expensive many-objective optimization
Knee solution
Pareto front estimation
Kriging model
Evolutionary multi-task optimization
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