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A new resource allocation strategy based on the relationship between subproblems for MOEA/D. (English) Zbl 1453.90156
Summary: Multi-objective evolutionary algorithms based on decomposition (MOEA/D) decomposes a multi-objective optimization problem (MOP) into a set of simple scalar objective optimization sub-problems and solves them in a collaborative way. Since the sub-problems are different in optimization difficulty and computational resource demanding, it is critical to reasonably allocate computational resources among them, which can optimize the usage of resources and improve the performance of an algorithm. This paper proposes a new resource allocation strategy based on the relationship between sub-problems for MOEA/D. A probability vector is maintained based on the relationship between sub-problems, which is used to guide the selection of sub-problems for optimization. In the optimization process, we explored the role of priority optimization of boundary sub-problems and used it to assist in the update of probability vector in the early optimization phase. A steady-state algorithm is designed and tested experimentally. The results suggest that the designed algorithms have some advantages over existing state-of-the-art algorithms.
90C29 Multi-objective and goal programming
68W50 Evolutionary algorithms, genetic algorithms (computational aspects)
90C59 Approximation methods and heuristics in mathematical programming
91B32 Resource and cost allocation (including fair division, apportionment, etc.)
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