Abstract
Large-scale sparse multi-objective optimization problems exist widely in the real world, but most existing evolutionary algorithms encounter great difficulties in solving the problems of this type, mainly due to the curse of dimensionality and the underutilized sparsity knowledge of the Pareto optimal solutions. To address these issues, this paper proposes a multi-stage knowledge-guided evolutionary algorithm for large-scale sparse multi-objective optimization problems, which aims to enhance the optimization capability by incorporating diversified sparsity knowledge into the evolutionary process. Specifically, three kinds of the knowledge are designed and an effective multi-stage evolutionary strategy based on knowledge fusion is developed to make full use of three kinds of knowledge. Experimental results on eight benchmark problems and three real-world problems demonstrate that the proposed algorithm outperforms the state-of-the-art approaches in terms of effectiveness and convergence speed.
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