Journal
COMPLEX & INTELLIGENT SYSTEMS
Volume 7, Issue 2, Pages 765-780Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s40747-020-00230-8
Keywords
Hyper-heuristics; Multi-objective optimization; Adaptive epsilon-greedy selection strategy; Cross-domain problems
Categories
Funding
- Technological Research Projects in Henan Province [192102210107]
- National Key RD Project [2020YFB1712401]
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The multi-objective hyper-heuristic algorithm HH_EG proposed in this paper is based on adaptive epsilon-greedy selection and can solve MOPs by selecting and combining low-level heuristics without parameter tuning, making it easy to integrate with various performance indicators. Experimental results demonstrate the effectiveness of HH_EG in combining the advantages of each LLH and solving cross-domain problems.
A variety of meta-heuristics have shown promising performance for solving multi-objective optimization problems (MOPs). However, existing meta-heuristics may have the best performance on particular MOPs, but may not perform well on the other MOPs. To improve the cross-domain ability, this paper presents a multi-objective hyper-heuristic algorithm based on adaptive epsilon-greedy selection (HH_EG) for solving MOPs. To select and combine low-level heuristics (LLHs) during the evolutionary procedure, this paper also proposes an adaptive epsilon-greedy selection strategy. The proposed hyper-heuristic can solve problems from varied domains by simply changing LLHs without redesigning the high-level strategy. Meanwhile, HH_EG does not need to tune parameters, and is easy to be integrated with various performance indicators. We test HH_EG on the classical DTLZ test suite, the IMOP test suite, the many-objective MaF test suite, and a test suite of a real-world multi-objective problem. Experimental results show the effectiveness of HH_EG in combining the advantages of each LLH and solving cross-domain problems.
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