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Landscape-Aware Performance Prediction for Evolutionary Multiobjective Optimization
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DOI：10.1109/TEVC.2019.2940828.png)
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Abstract
En 中文
We expose and contrast the impact of landscape characteristics on the performance of search heuristics for black-box multiobjective combinatorial optimization problems. A sound and concise summary of features characterizing the structure of an arbitrary problem instance is identified and related to the expected performance of global and local dominance-based multiobjective optimization algorithms. We provide a critical review of existing features tailored to multiobjective combinatorial optimization problems, and we propose additional ones that do not require any global knowledge from the landscape, making them suitable for large-size problem instances. Their intercorrelation and their association with algorithm performance are also analyzed. This allows us to assess the individual and the joint effect of problem features on algorithm performance, and to highlight the main difficulties encountered by such search heuristics. By providing effective tools for multiobjective landscape analysis, we highlight that multiple features are required to capture problem difficulty, and we provide further insights into the importance of ruggedness and multimodality to characterize multiobjective combinatorial landscapes.
Keywords:
Optimization
Prediction algorithms
Search problems
Correlation
Machine learning algorithms
Face
Linear programming
Black-box combinatorial optimization
evolutionary multiobjective optimization (EMO)
feature-based performance prediction
problem difficulty and landscape analysis
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