Improved SparseEA for sparse large-scale multi-objective optimization problems
نویسندگان
چکیده
Abstract Sparse large-scale multi-objective optimization problems (LSMOPs) widely exist in real-world applications, which have the properties of involving a large number decision variables and sparse Pareto optimal solutions, i.e., most these solutions are zero. In recent years, LSMOPs attracted increasing attentions evolutionary computation community. However, all recently tailored algorithms for put sparsity detection maintenance first place, where nonzero can hardly be optimized sufficiently within limited budget function evaluations. To address this issue, paper proposes to enhance connection between real binary two-layer encoding scheme with assistance variable grouping techniques. way, more efforts devoted part variables, achieving balance optimization. According experimental results on eight benchmark three proposed algorithm is superior over existing state-of-the-art LSMOPs.
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ژورنال
عنوان ژورنال: Complex & Intelligent Systems
سال: 2021
ISSN: ['2198-6053', '2199-4536']
DOI: https://doi.org/10.1007/s40747-021-00553-0