Fuzzy Adaptive Multitask Optimization

evolutionary multitask optimization (EMTO) aims to optimize multiple tasks simultaneously. In recent years, various EMTO algorithms based on knowledge transfer (KT) have been developed to utilize the information from other tasks and promote the optimization of the current task. However, most of them often use the fixed KT probability (ktp) and a single evolutionary search operator (ESO) during the evolution process, which lacks an adaption mechanism and cannot meet the different searching requirements among multiple tasks. Fuzzy system can effectively express the qualitative knowledge with unclear boundaries, which has good adaptability to nonindependent EMTO. Therefore, this article proposes a fuzzy adaptive multitask optimization (FAMTO), which employs a fuzzy adaptive transfer (FAT) strategy for intertask KT to achieve the adaptive adjustment of the ktp by designing a comprehensive evaluation in KT performance from two aspects, including the survival rate and the quality of transferred offspring. In FAT strategy, the fuzzy logical is employed to handle the interdependent relationships among multiple indicators, further achieving the more robust and adaptive ktp adjustment. In addition, an individual-based random selection (IRS) strategy is developed for each individual to choose the suitable ESO for intratask self-evolution in fuzzy adaptive multitasking optimization (FAMTO). Experimental results show that FAMTO achieves significantly better performance than other state-of-the-art EMTO algorithms on two well-known multitask benchmarks, CEC17 and CEC22. Furthermore, FAMTO is applied to a real-world multitask planar kinematic arm control application, demonstrating its applicability. Finally, the extended experiments on many-task optimization problems (MaTOPs) illustrate the scalability of FAMTO.