Decoupling Constraint: Task Clone-Based Multi-Tasking Optimization for Constrained Multi-Objective Optimization

The coupling of multiple constraints can pose difficulties in solving constrained multi-objective optimization problems (CMOPs). Existing constrained multi-objective evolutionary algorithms (CMOEAs) often overlook this issue by considering all constraints together. This article proposes MTOTC, a novel multi-tasking optimization algorithm that addresses this challenge through a task clone technique. MTOTC clones the target CMOP with q constraints into q+1 copies, resulting in a total of q+2 tasks. Each cloned task is handled using an independent population that considers a unique constraint-handling sequence, effectively decoupling the constraints in q+1 different ways. Additionally, the algorithm incorporates online information sharing between the target task and cloned tasks, enabling the utilization of valuable search history as much as possible. Experimental results on four recently developed complex CMOP benchmark suites and a series of real-world CMOPs demonstrate the superior performance of MTOTC compared to seven state-of-the-art CMOEAs.