Configuring the Perturbation Operations of an Iterated Local Search Algorithm for Cross-domain Search: A Probabilistic Learning Approach
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Abstract
Description
Hyper-heuristics are general-purpose heuristic search methodologies for solving
combinatorial optimization problems (COPs). Research findings have revealed that hyperheuristics
still suffer generalization issues as different strategies vary in performance from
an instance of a COP to another. In this paper, an approach based on Iterated Local Search
(ILS) is proposed to raise the level of generality of hyper-heuristics on the problem domains
of the HyFlex framework. The proposed approach utilizes a probabilistic learning technique
to automatically configure the behavior of the ILS algorithm during the perturbation stage of
the optimization process. In the proposed method, the mutation and ruin-recreate heuristics
are treated as distinct entities and the learning layer automatically determines the level of
utilization of these heuristic categories depending on the problem domain being solved. The
concept of double shaking is also presented where a solution can be perturbed twice before
the intensification phase. Experimental results reveal the level of generality achieved by the
proposed method as it recorded a minimum formula one score of 30.0 on each tested
problem domain. Direct comparison with a state-of-the-art ILS-based approach also
establishes the significance of the learning layer added to the perturbation stage of the ILS
metaheuristic. Finally, analysis of the perturbation behavior of the hyper-heuristic leads to
an interesting conclusion concerning the type of low-level heuristics that are highly
beneficial and non-beneficial to a given problem domain.
Keywords
QA75 Electronic computers. Computer science