Contribution-Driven Task Design: Multi-Task Optimization Algorithm for Large-Scale Constrained Multi-Objective Problems

Large-scale constrained multi-objective optimization problems (LSCMOPs) are highly challenging due to the need to optimize multiple conflicting objectives under complex constraints within a vast search space. To address this challenge, this paper proposes a multi-task optimization algorithm based on contribution-driven task design (MTO-CDTD). The algorithm constructs a multi-task optimization framework comprising one original task and multiple auxiliary tasks. Guided by an optimal contribution objective assignment strategy, each auxiliary task optimizes a subset of decision variables that contribute most to a specific objective function. A contribution-guided initialization strategy is then employed to generate high-quality initial populations for the auxiliary tasks. Furthermore, a knowledge transfer strategy based on multi-population collaboration is developed to integrate optimization information from the auxiliary tasks, thereby effectively guiding the original task in searching the large-scale decision space. Extensive experiments on three benchmark test suites—LIRCMOP, CF, and ZXH_CF—with 100, 500, and 1000 decision variables demonstrate that the proposed MTO-CDTD algorithm achieves significant advantages in solving complex LSCMOPs.