Application of Fractional Order ABC and GA for Neural Network Training and Clustering Process

Abstract

Literature presents several search algorithms to find an item with specified properties from a search space defined by a mathematical formula or procedure. One of the widely accepted algorithms is optimization algorithm which can find the optimal element within a certain period of time if the search space is defined. Recent works formulate several problems as optimization problems which were then solved by many optimization algorithms. Accordingly, in a previous paper, a hybrid optimization algorithm, called FAGA was proposed using fractional order Artificial Bee Colony (ABC) and Genetic Algorithm (GA) for optimization to solve the existing benchmark problems. In this paper, we have planned to apply the FAGA algorithm to well defined-real time problems of neural network training and the clustering process. Through neural network training, data classification will be done by making use of FAGA algorithm as neural network training procedure. Similarly, medical image segmentation will be done using clustering process through FAGA algorithm. The performance of the FAGA algorithm in those two processes will be evaluated with different evaluation metrics and the comparison of the FAGA algorithm will be also carried out with the existing ABC and genetic algorithm.

Keywords

• FAGA
• Neural Network
• Image Segmentation
• Data Classification