A Local Knowledge Transfer-Based Evolutionary Algorithm for Constrained Multitask Optimization

Evolutionary multitask optimization (EMTO) can solve multiple tasks simultaneously by leveraging the relevant information between tasks, but existing EMTO algorithms do not take into account the fact that almost all problems in the real world contain constraints. To address this dilemma, this article studies a local knowledge transfer-based evolutionary algorithm for constrained multitask optimization. To be specific, each task population is divided into multiple niches to enhance the diversity and control the intensity of knowledge transfer, thus avoiding excessive transfer of knowledge. Then a new similarity judgment method based on the information feedback of pioneer individuals is developed to judge the similarity between tasks and whether to perform knowledge transfer. Furthermore, two different transfer methods: a direct transfer and a learning transfer, are devised to perform knowledge transfer among niches pertaining to different tasks. In addition, an excellent-information-guided mutation mechanism is proposed to prevent niches from getting trapped in local optima and to promote rapid convergence. The system experiment on 18 constrained multitask test instances and 2 real-world problems demonstrate that the proposed algorithm outperforms or is at least comparable to other EMTO algorithms and constrained single-objective optimization algorithms.