An Efficient Modified Artificial Bee Colony Algorithm for Job Scheduling Problem
Manish Gupta1, Govind sharma2
Manuscript received on December 07, 2011. | Revised Manuscript received on December 23, 2011. | Manuscript published on January 05, 2012. | PP: 291-296 | Volume-1 Issue-6, January 2012. | Retrieval Number: F0331121611/2012©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Swarm intelligence systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. Particle swarm, Ant colony, Bee colony are examples of swarm intelligence. In the field of computer science and operations research, Artificial Bee Colony Algorithm (ABC) is an optimization algorithm based on the intelligent foraging behavior of honey bee swarm. The job scheduling problem is the problem of assigning the jobs in the system in a manner that will optimize the overall performance of the application, while assuring the correctness of the result. In this paper, An Efficient artificial bee colony (ABC) algorithm, where we have used additional mutation and crossover operator of Genetic algorithm (GA) in the classical ABC algorithm. We have added crossover operator after the employed bee phase and mutation operator after onlooker bee phase of ABC algorithm, is proposed in this paper, for solving the job scheduling problem with the criterion to decrease the maximum completion time. The simulated results show that ABC proves to be a better algorithm when applied to job scheduling problem. General Terms Algorithms, Experimentation, Verification.
Keywords: Artificial Bee Colony, ABC, Genetic Algorithm, GA, Mutation, crossover.