Multi-regularization sparse reconstruction based on multifactorial multiobjective optimization

Abstract

In recent sparse reconstruction, the sparsity and reconstruction error can be considered as two objectives and tackled by multiobjective optimization methods. Since the sparse reconstruction problem can be modeled in multiple regularization forms, it can be addressed in a multifactorial multiobjective optimization paradigm by the evolutionary multitasking approach to transfer useful information across multiple regularization forms to help solve the problem. In this paper, a multi-regularization based on multifactorial multiobjective optimization is proposed to solve the sparse reconstruction problem. First, the sparse reconstruction problem is constructed as a multi-regularization model. Then, this model is optimized by a multifactorial multiobjective optimization method. In the evolutionary process, considering the priority of different regularization in the multi-regularization model, a preference-based selection method is designed. In addition, to accommodate the sparsity characteristic of the sparse reconstruction problem, a sparsity-oriented crossover operator is performed. Finally, an iterative-thresholding-based local search is incorporated into the algorithm to improve the convergence performance. Experiments on multiple datasets and image reconstruction tasks demonstrate the effectiveness and practicality of the proposed algorithm.