Differential Evolution with Dynamic Adaptation of Parameters for the Optimization of Fuzzy Controllers

This work is mainly focused on improving the differential evolution algorithm with the utilization of shadowed and general type 2 fuzzy systems to dynamically adapt one of the parameters of the evolutionary method. In this case, the mutation parameter is dynamically moved during the evolution process by using a shadowed and general type-2 fuzzy systems. The main idea of this work is to make a performance comparison between using shadowed and general type 2 fuzzy systems as controllers of the mutation parameter in differential evolution. The performance is compared with the problem of optimizing fuzzy controllers for a D.C. Motor. Simulation results show that general type-2 fuzzy systems are better when higher levels of noise are considered in the controller.

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DIFFERENTIAL EVOLUTION WITH DYNAMIC ADAPTATION OF PARAMETERS BASED ON A FUZZY LOGIC AUGMENTATION APPROACH

Journal of Universal Mathematics ◽

10.33773/jum.523748 ◽

2019 ◽

Vol 2 (2) ◽

pp. 183-207

Author(s):

Oscar Castillo ◽

Patricia Ochoa ◽

José Soria

Keyword(s):

Fuzzy Logic ◽

Differential Evolution ◽

Dynamic Adaptation

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Shadowed Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search and Differential Evolution Algorithms

Algorithms ◽

10.3390/a12010017 ◽

2019 ◽

Vol 12 (1) ◽

pp. 17 ◽

Cited By ~ 19

Author(s):

Oscar Castillo ◽

Patricia Melin ◽

Fevrier Valdez ◽

Jose Soria ◽

Emanuel Ontiveros-Robles ◽

...

Keyword(s):

Differential Evolution ◽

Fuzzy Inference ◽

Harmony Search ◽

Dynamic Parameter ◽

Optimization Methods ◽

Dynamic Adaptation ◽

Fuzzy Inference Systems ◽

Parameter Adaptation ◽

Inference Systems

Nowadays, dynamic parameter adaptation has been shown to provide a significant improvement in several metaheuristic optimization methods, and one of the main ways to realize this dynamic adaptation is the implementation of Fuzzy Inference Systems. The main reason for this is because Fuzzy Inference Systems can be designed based on human knowledge, and this can provide an intelligent dynamic adaptation of parameters in metaheuristics. In addition, with the coming forth of Type-2 Fuzzy Logic, the capability of uncertainty handling offers an attractive improvement for dynamic parameter adaptation in metaheuristic methods, and, in fact, the use of Interval Type-2 Fuzzy Inference Systems (IT2 FIS) has been shown to provide better results with respect to Type-1 Fuzzy Inference Systems (T1 FIS) in recent works. Based on the performance improvement exhibited by IT2 FIS, the present paper aims to implement the Shadowed Type-2 Fuzzy Inference System (ST2 FIS) for further improvements in dynamic parameter adaptation in Harmony Search and Differential Evolution optimization methods. The ST2 FIS is an approximation of General Type-2 Fuzzy Inference Systems (GT2 FIS), and is based on the principles of Shadowed Fuzzy Sets. The main reason for using ST2 FIS and not GT2 FIS is because the computational cost of GT2 FIS represents a time limitation in this application. The paper presents a comparison of the conventional methods with static parameters and the dynamic parameter adaptation based on ST2 FIS, and the approaches are compared in solving mathematical functions and in controller optimization.

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