A novel membrane-inspired evolutionary framework for multi-objective multi-task optimization problems

In recent years, many different membrane-inspired evolutionary algorithms have been proposed to solve various complex optimization problems. Considering membrane systems' powerful computing performance and parallel capability, it has outstanding potential in solving multi-task optimization problems. However, there is no research to explore the performance of membrane-inspired evolutionary algorithms in solving multi-task optimization problems. In this paper, a novel membrane-inspired evolutionary framework with a hybrid dynamic membrane structure is proposed to solve the multi-objective multi-task optimization problems. First, a novel membrane-inspired two-stage evolution strategy algorithm is proposed as the algorithm in the membrane to improve the convergence of the algorithm and the diversity of multisets. Second, the information molecule concentration vector is proposed to reduce negative information transfer. The information molecule concentration vector is inspired by the binding process of information molecules and receptors and can control the information transfer probability adaptively. Finally, comprehensive experimental results show that the proposed algorithm performs better than most advanced multi-objective evolutionary multitasking algorithms. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

In this research, a novel membrane-inspired evolutionary framework with a hybrid dynamic membrane structure is proposed to solve multi-objective multi-task optimization problems. The algorithm improves convergence and diversity, and reduces negative information transfer through the information molecule concentration vector.