Abstract
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.
Highlights
Multi-objective optimization problems (MOPs) with m objectives can be described as Min F(x) (f 1(x), ..., f m(x)), x ∈ Ω [29]
In “Experimental results on DTLZ”, we evaluate the effectiveness of heuristic algorithm based on adaptive epsilon-greedy selection (HH_EG) by controlling three level heuristics (LLHs), H {h1, h2, h3} on the DTLZ test suite
In “Experimental results on IMOP” and “Experimental results on MaF”, we evaluate the cross-domain ability of HH_EG by solving the two novel test suites, IMOP and MaF
Summary
Multi-objective optimization problems (MOPs) with m objectives can be described as Min F(x) (f 1(x), ..., f m(x)), x ∈ Ω [29]. When solving MOPs, the commonly used heuristics are multi-objective evolution algorithms (MOEAs) [5]. A hyper-heuristic (HH) is a methodology which has with cross-domain search abilities This methodology is a highlevel strategy by controlling a set of low-level heuristics (LLHs). This study proposes an adaptive epsilon-greedy selection based multi-objective hyper-heuristic algorithm (HH_EG) that applies multiple MOEAs as LLHs. In each iteration, HH_EG uses an online-learning strategy to score each LLH and utilizes an adaptive epsilon-greedy selection strategy to select one LLH based on the scores. The MaF [3] test suite with 5 and 10 objectives are tested with NSGA-III [8], MOEA/D and MSEA as LLHs. Experimental results show the advantages of HH_EG and its cross-domain search abilities
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