Competitive Multitasking for Computational Resource Allocation in Evolutionary Constrained Multi-Objective Optimization

Constrained multi-objective optimization problems (CMOPs) have multiple objective functions that need to be optimized and constraints need to be satisfied, making them difficult to solve. Based on the multitasking optimization, the optimization of the original CMOP can be transformed into multiple related sub-tasks. Existing multitasking-based constrained multi-objective optimization evolutionary algorithms assist the evolution of the original problem by adopting auxiliary tasks. However, this approach may waste computational resources on tasks that are unsuitable for evolutionary states and dynamics. In this paper, a new competitive multitasking-based framework is proposed for CMOPs. We maintain an archive for the constrained Pareto front and multiple sub-tasks as auxiliaries. In each iteration, one of the sub-tasks is selected as the main task, and offspring are generated from its evolution. The offspring are viewed as knowledge and fed back to auxiliary tasks. The reward is mapped to a selection probability to control the main task selection in each iteration. Computational resources are saved by allocating only to the main task that is better suited for different evolutionary stages of different problems. The effectiveness of our approach is validated through experiments on four CMOP benchmark suites compared to eleven state-of-the-art methods.