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An Analytical Study for the Role of Fuzzy Logic in Improving Metaheuristic Optimization Algorithms

Vij Sonakshi, Jain Amita, Tayal Devendra, Castillo Oscar

Journal of Automation Mobile Robotics and Intelligent Systems

2018

Vol. 12, No. 4

11--27

The research applications of fuzzy logic have always been multidisciplinary in nature due to its ability in handling vagueness and imprecision. This paper presents an analytical study in the role of fuzzy logic in the area of metaheuristics using Web of Science (WoS) as the data source. In this case, 178 research papers are extracted from it in the time span of 1989-2016. This paper analyzes various aspects of a research publication in a scientometric manner. The top cited research papers, country wise contribution, topmost organizations, top research areas, top source titles, control terms and WoS categories are analyzed. Also, the top 3 fuzzy evolutionary algorithms are extracted and their top research papers are mentioned along with their topmost research domain. Since neuro fuzzy logic poses feasible options for solving numerous research problems, hence a section is also included by the authors to present an analytical study regarding research in it. Overall, this study helps in evaluating the recent research patterns in the field of fuzzy metaheuristics along with envisioning the future trends for the same. While on one hand this helps in providing a new path to the researchers who are beginners in this field as they can start exploring it through the analysis mentioned here, on the other hand it provides an insight to professional researchers too who can dig a little deeper in this field using knowledge from this study.

Fuzzy evolutionary algorithms and neural networks in uncertain optimization problems

Orantek P.

Computer Assisted Mechanics and Engineering Sciences

2007

Vol. 14, No. 2

317-329

This paper is devoted to the application of the evolutionary algorithms and artificial neural networks to uncertain optimization problems in which some parameters are described by fuzzy numbers. The special method of global optimization: Two-Stages Fuzzy Strategy (TSFS) for structures in uncertain conditions is proposed. As the first stage of the TSFS the fuzzy evolutionary algorithm is used. As the second stage the local optimization method with neuro-computing is proposed. The presented approach is applied in the identification problems of mechanical structures, in which material parameters and loadings are uncertain. To solve the direct problem the fuzzy boundary element method (FBEM) is used. Several numerical tests and examples are presented.