Choice function based hyper-heuristics for multi-objective optimization
Journal Article
Maashi, Mashael S. . 2015
Magazine \ Newspaper:
Applied Soft Computing
Volume Number:
28
Pages:
312–326
Publication Abstract:
tA selection hyper-heuristic is a high level search methodology which operates over a fixed set of low levelheuristics. During the iterative search process, a heuristic is selected and applied to a candidate solutionin hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems,while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initialstudies on selection hyper-heuristics combining a choice function heuristic selection methodology withgreat deluge and late acceptance as non-deterministic move acceptance methods for multi-objectiveoptimization. A well-known hypervolume metric is integrated into the move acceptance methods toenable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as areal-world multi-objective problem. The experimental results demonstrate the effectiveness of the non-deterministic move acceptance, particularly great deluge when used as a component of a choice functionbased selection hyper-heuristic.