Abstract
The algorithms based on decomposition are regarded as a promising optimizer for the multi-objective optimization problems (MOPs). However, it is difficult for the algorithms based on decomposition to handle MOPs with the complicated feature, because they adopt the fixed weight vectors. In this paper, we propose an adaptive method with the region detection strategy for the decomposition-based evolutionary algorithm (aMOEA/D-RD) to adjust the weight vectors. In the proposed algorithm, some useless weight vectors, which are not associated to any solution in the successive generations, are found through the region detection method. Then these weight vectors are adaptively adjusted by the solutions in the most crowed subregion. After the adjustment of weight vectors, the distribution of the weight vectors can be more suit to approximate the true the Pareto optimal front of MOPs. Comparative experiments on benchmark with various geometric features have been performed and the simulation results show the effectiveness and the competitiveness of our proposal algorithm.
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