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Selection hyper-heuristics for the multi and many-objective quadratic assignment problem
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DOI：10.1016/j.cor.2022.105961.png)
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摘要
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Hyper-heuristics (HH) emerged as more generalized and robust solutions for combinatorial optimization, being successfully addressed to solve several real-world problems. Implemented within an association of MOEA/DD and Differential Evolution, four selection hyper-heuristics (high-level heuristics) are studied in this work: Thompson Sampling, Probability Matching, Adaptive Pursuit and Self-Adaptive Differential Evolution. In the proposal, low-level heuristics rely on crossover performed by operators taken from a candidate pool. The HH selection is based on operators' previous performance during the evolutionary process, using a warm-up phase necessary to provide proper information regarding the most efficient operators. A discard mechanism is also considered to eliminate from the pool operators with similar performance. To evaluate the proposed approach, Quadratic Assignment Problem (QAP) instances are considered with 2, 3, 5, 7 and 10 objectives, totaling 148 instances with different dimensions and correlations between the flow matrices. Statistical tests indicate that the best version of the proposed approach, named HHMOEA/DD, outperforms those with fixed crossover operator and different literature approaches. In addition, the experiments indicate results' improvement by the joined inclusion of the warm-up and operator discard mechanisms.
Keyword:
Multi-objective optimization
Many-objective optimization hyper-heuristics
mQAP
MaQAP
期刊
C
IF:
4.3
论文数:
6.5K
被引数:
1.8W
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