Multitasking Genetic Algorithm (MTGA) for Fuzzy System Optimization

作     者：Wu, Dongrui Tan, Xianfeng 

作者机构：Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON FUZZY SYSTEMS》 (IEEE模糊系统汇刊)

年 卷 期：2020年第28卷第6期

页      面：1050-1061页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Technology Innovation Project of Hubei Province of China [2019AEA171] 

主　　题：Task analysis Biological cells Optimization Sociology Statistics Evolutionary computation Genetic algorithms Evolutionary multitasking fuzzy logic controller (FLC) genetic algorithm multifactorial optimization (MFO) multitask learning 

摘      要：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.