A multiple knowledge-based evolutionary algorithm for sparse large-scale multi-objective problems

In real-world applications, sparse large-scale multi-objective optimization problems (LSMOPs) are prevalent. Sparse LSMOPs are the LSMOPs characterized by Pareto-optimal solutions with sparse decision variables. Nonetheless, limited attention has been given to developing general-purpose algorithms for sparse LSMOPs. Existing approaches primarily focus on detecting sparsity, whereas achieving optimal results requires accurate detection of sparse distributions and simultaneous optimization of non-zero variables. Therefore, this paper proposes a multiple knowledge-based sparse large-scale multi-objective evolutionary algorithm. Building on genetic algorithms, the proposed algorithm employs a two-layer encoding scheme, develops a knowledge-driven evolution strategy for optimizing binary vectors, and introduces an association optimization method for optimizing real vectors. The performance of the proposed algorithm is assessed using both benchmark tests and real-world applications. Experimental results demonstrate that the proposed algorithm is competitively effective in solving sparse LSMOPs.