Power line faults classification by neural network train by Ant Colony Optimization

This proposal concentrates on arrangement of electrical cable issues. Flaws arrangements have been accomplish by utilizing manufactured neural systems. The diverse blames on the electrical cable ought to be arranged and found effectively. In this paper, the selected method is to train the neural network with the Ant Colony Optimization. In This paper it will show the performance for Neural Network and the performance train with Ant Colony Optimization for case of classification. Ant Colony Optimization is a meta-heuristic way to deal with take care of troublesome streamlining issues. Preparing a neural system is a procedure of finding the ideal set. An Ant Colony Optimization (ACO) is utilized to prepare with the neural systems. In this venture, the Ant Colony Optimization (ACO) calculation used to prepare neural system was studies, usage and tried with preparing issue. The execution of this Ant Colony Optimization (ACO) usage was contrasted and that nonpartisan system and discovered it is more compelling than neural system. Metaheuristic algorithms are algorithms which, in order to escape from local optima, drive some basic heuristic: either a constructive heuristic starting from a null solution and adding elements to build a good complete one, or a local search heuristic starting from a complete solution and iteratively modifying some of its elements in order to achieve a better one. The metaheuristic part permits the low level heuristic to obtain solutions better than those it could have achieved alone, even if iterated. The characteristic of ACO algorithms is their explicit use of elements of previous solutions. This paper will show the best performance of Ant Colony Optimization compared to Neural Network.