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With the very fast development in today`s digital world, Information Retrieval on Internet is gaining importance, day by day. The web comprises of huge amount of data and search engine provides an efficient way to navigate the web and get the relevant information. The search engine has proven to be less efficient in providing relevant information from a query processed by a user. Fors olving this problem and getting accurate results there is need to categorize these web pages. Many optimizations have also done to speedup the classification process as it is required to be fast while maintaining the efficiency. To maintain the accuracy with the lesser time requirement, researchers have developed a SVM based Layered approach with the help of firefly feature selection method.

This paper built a optimization model and proposed an improved firefly optimization algorithm called CFA, which is based on firefly Cooperative. The main idea of CFA is to extend the single population FA to the interacting multi-swarms by cooperative Models. In this work, firstly, CFA algorithm is used for optimizing six widely-used benchmark functions and the comparative results produced by, firefly optimization algorithm(FA) are studied. Secondly, CFA algorithm used in data mining, clustering analysis on several typical data sets. The performance of typical data clustering results showed that the biological heuristic algorithm based on clustering analysis algorithm with the existing success of FA compared to faster convergence, and the clustering of higher quality.

A light water nuclear Reactor has been exergy analyzed, and the rate of irreversible exergy loss and exergy destruction is calculated for each of its components. The ratio of these losses compared to the total input exergy loss is calculated, which shows that most irreversible losses occur in the reactors, turbines, steam generators, respectively, as well as in the downstream operations. The main aim of this paper is to optimize the power plant using an innovative firefly algorithm and then to propose a novel strategy to improve the overall performance of the plant. As shown in the results, the exergy destruction rate of the plant decreased by 1.18% using the firefly method, and the exergy efficiency of the plant reached 29.3% comparing to the operational amount of 28.99%. Also, the results of the firefly optimization process compared to the Genetic algorithm and gravitational search algorithm to study the accuracy of the model for exergy analysis fitness problems in the power plants and the results of this comparison has shown that the results are nearly similar in the mentioned methods. However, the firefly is faster and more accurate in limited iterations.

Friction stir welding (FSW) is a green manufacturing process that does not liberate smoke, fume and odour unlike the conventionalarc welding. This research article aims at finding the ultimate tensile strength of the Aluminium matrix compositeswelded by FSW with the process parameters such as tool rotation speed, weld traverse speed and axial force. The searchoptimization is carried out in two phases using MATLAB environment. Firstly, the regression equation of the experimentsis utilized to find the better design points by Genetic Algorithm (GA) through pool generation, cross-over and mutation. Secondly, the top design points obtained in GA are stored in a new pool, from which the global best optimal design is selectedby Firefly Algorithm (FA). Since, every algorithm has different features and highlights, the coupled GA-FA algorithm isutilized to obtain the optimal point that gives the best ultimate tensile strength of the welded composite. The results demonstratethat the optimal points are distributed in several points of design space that needs to be searched out by the effectiveoptimization strategy. The convergence rate, speed of the optimization and coverage of the design points are also improved. The algorithm shows good agreement with the confirmation tests also with errors less than 5%.