Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 28, Issue 6, Pages 1050-1061Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2020.2968863
Keywords
Task analysis; Biological cells; Optimization; Sociology; Statistics; Evolutionary computation; Genetic algorithms; Evolutionary multitasking; fuzzy logic controller (FLC); genetic algorithm; multifactorial optimization (MFO); multitask learning
Funding
- National Natural Science Foundation of China [61873321]
- Technology Innovation Project of Hubei Province of China [2019AEA171]
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Multitask learning uses auxiliary data or knowledge from relevant tasks to facilitate the learning in a new task. Multitask optimization applies multitask learning an optimization to study how effectively and efficiently tackle the multiple optimization problems, simultaneously. Evolutionary multitasking, or multifactorial optimization, is an emerging subfield of multitask optimization, which integrates evolutionary computation and multitask learning. This article proposes a novel and easy-to-implement multitasking genetic algorithm (MTGA), which copes well with significantly different optimization tasks by estimating and using the bias among them. Comparative studies with eight state-of-the-art single-task and multitask approaches in the literature on nine benchmarks demonstrated that, on average, the MTGA outperformed all of them and had lower computational cost than six of them. Based on the MTGA, a simultaneous optimization strategy for fuzzy system design is also proposed. Experiments on simultaneous optimization of type-1 and interval type-2 fuzzy logic controllers for couple-tank water level control demonstrated that the MTGA can find better fuzzy logic controllers than other approaches.
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