Multiple sparse detection-based evolutionary algorithm for large-scale sparse multiobjective optimization problems

Sparse multiobjective optimization problems are common in practical applications. Such problems are characterized by large-scale decision variables and sparse optimal solutions. General large-scale multiobjective optimization problems (LSMOPs) have been extensively studied for many years. They can be well solved by many excellent custom algorithms. However, when these algorithms are used to deal with sparse LSMOPs, they often encounter difficulties because the sparse nature of the problem is not considered. Therefore, aiming at sparse LSMOPs, an algorithm based on multiple sparse detection is proposed in this paper. The algorithm applies an adaptive sparse genetic operator that can generate sparse solutions by detecting the sparsity of individuals. To improve the deficiency of sparse detection caused by local detection, an enhanced sparse detection (ESD) strategy is proposed in this paper. The strategy uses binary coefficient vectors to integrate the masks of nondominated solutions. Essentially, the mask is globally and deeply optimized by coefficient vectors to enhance the sparsity of the solutions. In addition, the algorithm adopts an improved weighted optimization strategy to fully optimize the key nonzero variables to balance exploration and optimization. Finally, the proposed algorithm is named MOEA-ESD and is compared to the current state-of-the-art algorithm to verify its effectiveness.

Automated summary

Sparse multiobjective optimization problems are common in practical applications and are characterized by large-scale decision variables and sparse optimal solutions. This paper proposes an algorithm based on multiple sparse detection to address these problems, which can generate sparse solutions by detecting the sparsity of individuals. An enhanced sparse detection strategy using binary coefficient vectors is also proposed to improve the deficiency of local detection. Additionally, the algorithm adopts an improved weighted optimization strategy to balance exploration and optimization. The proposed algorithm, named MOEA-ESD, is compared to the current state-of-the-art algorithm to verify its effectiveness.