Abstract
AbstractLiterature 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 c...
Highlights
As the extent of advanced in data collection becomes exponentially huge volumes of crude information need to be extricated
In Ref. 23 we proposed Fractional Order ABC and GA (FAGA) and obtained better minimization and convergence rate using different functions as fitness calculation
After training the neural network based on FAGA, the dataset is given as input to the neurons in the input layer of neural network
Summary
As the extent of advanced in data collection becomes exponentially huge volumes of crude information need to be extricated. A genetic algorithm is generally said to converge when there is no critical change in the estimations of fitness of the population from one generation to the Both ABC and GA have local optimization problem. The population of the positions is subjected to repeated cycles of the search processes of the employed bees, the onlooker bees and scout bees In this phase, selection of the food sources by the onlookers after receiving the information of employed bees and generation of new solution based on fractional calculus is carried out. The control parameters used in the algorithm consist of the number of the food sources which is equal to the number of employed or onlooker bees, the value of limit, mutation operation and the maximum cycle number. Fig.[1] shows the process of segmenting an image using FAGA
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
