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.

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

Automatic filtering of information has become increasingly important in recent years due to large availability of electronic information. We present an approach for optimization of Information Retrieval System (IRS) by genetic algorithm and fuzzy sets in an adaptive filtering process. From the observed user preferences regarding documents in the sets retrieved, the system learns from the user's information needs. An interest profile is created that represents the needs as learned from the observed preferences in the user's area of interest. The proposed system will act as an offline information-filtering agent. The documents have been already downloaded from the Internet using google. The system generates a recommendation based on adaptive filtering using a set of keywords extracted from all documents evaluated by user. The process starts with the initial set of documents retrieved as the answer to user's Initial query in the area of interest. The preferences given by the user are learned through explicit feedback on retrieved documents. The agent filters and ranks the retrieved information according to user's preferences using Genetic Algorithm (GA) and Fuzzy Set Theory. The system has been implemented using Java on Windows 98.

Abha Khandelwal

Papers

Information filtering using fuzzy-genetic algorithm approach