Decomposition based multiobjective hyper heuristic with differential evolution

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-HHSW, 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-HHSW 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-HHSW was tested in a well established set of 10 instances from the CEC 2009 MOEA Competition. MOEA/D-HHSW was favourably compared with state-of-the-art multiobjective optimization algorithms.