Comparative Study between Type-1 and Interval Type-2 Fuzzy Systems in Parameter Adaptation for the Cuckoo Search Algorithm

The objective of this work is focused on improving the optimization ability of the cuckoo search algorithm (CS), and, for this reason, a comparison is made between type-1 and interval type-2 fuzzy logic to look for more promising results in the cuckoo search algorithm (CS), and to help performance,...

Full description

Saved in:
Bibliographic Details
Published inSymmetry (Basel) Vol. 14; no. 11; p. 2289
Main Authors Guerrero, Maribel, Valdez, Fevrier, Castillo, Oscar
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2022
Subjects
Adaptation
Algorithms
Artificial intelligence
Comparative studies
Control systems design
Decision making
Fuzzy logic
Fuzzy sets
Fuzzy systems
Linguistics
Optimization
Parameters
Process controls
Real variables
Search algorithms
Set theory
Online AccessGet full text
ISSN2073-8994
2073-8994
DOI10.3390/sym14112289

Cover

More Information
Summary:The objective of this work is focused on improving the optimization ability of the cuckoo search algorithm (CS), and, for this reason, a comparison is made between type-1 and interval type-2 fuzzy logic to look for more promising results in the cuckoo search algorithm (CS), and to help performance, we dynamically adjust the alpha parameter. The idea is to enable CS in leaving the local optima, and then be able to reach the global optima. Currently, there are good results in improving the optimization of algorithms through intelligent fuzzy logic computing after finding the best adjustment parameters. The approach is based on finding the ideal rules with their respective linguistic variables to represent the real world as is perceived by humans. The membership functions that the fuzzy system uses are symmetrically defined for reducing the search space, and this symmetry is what makes the algorithm efficient. We plan to test the proposal in future works in the optimal design of control systems. In the present study, we use five benchmark mathematical functions with variation in the number of dimensions to validate the approach and perform the comparison of interval type-2 and type-1 fuzzy systems in parameter adaptation. For the dynamic adjustment of the parameters, we select the alpha parameter, and the values of Pa and Beta are defined based on the analysis of their behavior in previous works.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2073-8994
2073-8994
DOI:10.3390/sym14112289