Recent advances on the use of meta-heuristic optimization algorithms to optimize the type-2 fuzzy logic systems in intelligent control

被引：33
作者：
Hamza, Mukhtar Fatihu [1 ,2 ]
Yap, Hwa Jen [1 ]
Choudhury, Imtiaz Ahmed [1 ]
机构：
[1] Univ Malaya, Dept Mech Engn, Kuala Lumpur 50603, Malaysia
[2] Bayero Univ, Dept Mechatron Engn, Kano 3011, Nigeria
来源：
NEURAL COMPUTING & APPLICATIONS | 2017年 / 28卷 / 05期
关键词：
Type-2 fuzzy logic systems; Intelligent control; Genetic algorithm; Particle swarm optimization; Hybrid meta-heuristic algorithms; PARTICLE SWARM OPTIMIZATION; HYBRID GENETIC ALGORITHM; INTERVAL TYPE-2; OPTIMAL-DESIGN; MOBILE ROBOT; REDUCTION; SETS; CLASSIFICATION; AGGREGATION; PERFORMANCE;
D O I：
10.1007/s00521-015-2111-9
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Finding the appropriate values of parameters and structure of type-2 fuzzy logic systems is a difficult and complex task. Many types of meta-heuristic algorithms have been used to find the complex structure and appropriate parameter values of the type-2 fuzzy systems and more recently hybrid meta-heuristic algorithms. In this paper, we review recent advances (2012 to date) on the application of meta-heuristic algorithms and hybrid meta-heuristic algorithms, for the optimization of type-2 fuzzy logic systems in intelligent control. It was found that the major meta-heuristic algorithms used for optimizing the design of type-2 fuzzy logic systems in intelligent control were genetic algorithms and particle swarm optimization as well as hybrid meta-heuristic algorithms. Researchers can use this review as a starting point for further advancement as well as an exploration of other meta-heuristic algorithms that have received little or no attention from researchers.
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收藏
页码：979 / 999
页数：21
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