APPLICATION OF ARTIFICIAL BEE COLONY ALGORITHM USING HADOOP

Abstract

ABSTRACT Optimization problem is a very common problem of finding the best solution from all feasible solutions. It is also a very common problem both in theoretical and practical scenes. In most cases optimization problem is also been classified as a NP-Hard problem. In today’s era, time and computing resources are most common limitations. People often choose such metaheuristic methods that evaluate the optimal value in a limited time and with a limited computing resource. Artificial Bee Colony (ABC) is newly proposed metaheuristic algorithm which is suggested very compromising in recent work. As compared with the other metaheuristic algorithms, ABC is very effective for high dimensional problems. ABC can also solve various optimization problems with complex nonlinearity because it is a fast and robust algorithm. In this work, we have proposed a parallel implementation of Artificial Bee Colony algorithm for analysis of big data by using MapReduce paradigm. During the implementation of this work, Hadoop has been used as the backend MapReduce platform. As the result shows our ABC algorithm is very effective and scalable.