Turning Sparse Large-Scale Multiobjective Optimization Into Evolutionary Multitasking

Sparse large-scale multiobjective optimization problems (SLSMOPs) frequently emerge in diverse artificial intelligence applications. They are characterized by a high-dimensional search space where only a small subset of decision variables are non-zero. Many existing algorithms aim to concurrently identify zero-valued variables and optimize the non-zero subset within a reduced search space. However, striking an effective balance between these two aspects often proves elusive. To address this, we propose turning SLSMOPs into evolutionary multitasking, culminating in the development of a novel optimization framework, SparseEMT. This framework organizes the optimization process into three interrelated tasks based on the importance of variables. The first auxiliary task emphasizes fine-grained exploration of both zero and non-zero variables within a low-dimensional space. The second auxiliary task narrows the focus to a detailed search of only non-zero variables in an even lower-dimensional space. Finally, the main task concentrates on searching within the original high-dimensional space. In this framework, the entire population is divided into three segments, each dedicated to a specific task. Individuals undergo crossover and mutation both within their assigned tasks and across different tasks, facilitated by a specialized knowledge transfer strategy. Extensive empirical studies show that SparseEMT outperforms state-of-the-art algorithms on both the benchmark test suite and real-world applications, making it an effective solution for SLSMOPs.