Comparative Study of GA and ABC for Job Scheduling
V. Selvi1, R. Umarani2
1V.Selvi, Associate Professor, Department of M.C.A, M.A.M College Of Engineering, Siruganur, Trichy(Dt),Tamil Nadu, India.
2R.Umarani, Associate Professor, Department of Computer Science, Sri Saradha College For Women, Salem, Tamil Nadu, India.
Manuscript received on January 01, 2013. | Revised Manuscript received on January 02, 2013. | Manuscript published on January 05, 2013. | PP: 154-157 | Volume-2, Issue-6, January 2013. | Retrieval Number: D094071411/2013©BEIESP
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© 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: In the field of computer science and operation’s research, Artificial Bee Colony (ABC) is an optimization algorithm relatively new swarm intelligence technique based on behaviour of honey bee swarm and Meta heuristic. It is successfully applied to various paths mostly continuous optimization problems. Swarm intelligence systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. 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. ABC algorithm, is proposed in this paper, for solving the job scheduling problem with the criterion to decrease the maximum completion time. In this paper, modifications to the ABC algorithm is based on Genetic Algorithm (GA) crossover and mutation operators. Such modifications applied to the creation of new candidate solutions improved performance of the algorithm.
Keywords: Artificial Bee Colony, Genetic algorithm, Job scheduling.