Multiknowledge Adaptive Transfer Framework and Auxiliary Value Assessment Strategy for Constrained Multiobjective Optimization

Constrained multiobjective optimization problems (CMOPs) are widely present in the real world, and the key difficulty in solving CMOPs lies in effectively handling the interplay between satisfying constraints and optimizing conflicting objectives. Many constrained multiobjective optimization evolutionary algorithms (CMOEAs) have been proposed for solving CMOPs, among which the evolutionary multitasking (EMT)-based CMOEAs have demonstrated notable performance. However, most existing EMT-based CMOEAs focus on designing reasonable auxiliary tasks but do not address how to effectively facilitate knowledge transfer between multiple tasks. Building on these considerations, this article proposes a multiknowledge adaptive transfer framework that incorporates two novel categories of knowledge: synchronization knowledge in the decision space and evolutionary direction knowledge. This approach can learn more effectively from other populations compared to traditional EMT-based CMOEAs. Moreover, to prevent the auxiliary population from failing to provide effective value to the main population and wasting computational resources, a reward-based auxiliary value assessment strategy is proposed. Whether to terminate the reproduction of the auxiliary population is evaluated by the value of the auxiliary population to the main population. Based on these two improvements, this article introduces a novel multiknowledge adaptive transfer framework for constrained multiobjective optimization (MKATCMO). The experimental results on 33 benchmark test problems and 28 practical engineering application problems demonstrate that the proposed MKATCMO has excellent performance compared with nine state-of-the-art (SOTA) CMOEAs.