Abstract
Hyper-Heuristics is a high-level methodology for selection or generation of heuristics for solving complex problems. Despite their success, there is a lack of multi-objective hyper-heuristics. Our approach, named MOEA/D-HH\(_{SW}\), is a multi-objective selection hyper-heuristic that expands the MOEA/D framework. MOEA/D decomposes a multiobjective optimization problem into a number of subproblems, where each subproblem is handled by an agent in a collaborative manner. MOEA/D-HH\(_{SW}\) uses an adaptive choice function with sliding window proposed in this work to determine the low level heuristic (Differential Evolution mutation strategy) that should be applied by each agent during a MOEA/D execution. MOEA/D-HH\(_{SW}\) was tested in a well established set of 10 instances from the CEC 2009 MOEA Competition. MOEA/D-HH\(_{SW}\) was favourably compared with state-of-the-art multi-objective optimization algorithms.
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Gonçalves, R.A., Kuk, J.N., Almeida, C.P., Venske, S.M. (2015). Decomposition Based Multiobjective Hyper Heuristic with Differential Evolution. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9330. Springer, Cham. https://doi.org/10.1007/978-3-319-24306-1_13
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DOI: https://doi.org/10.1007/978-3-319-24306-1_13
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