Comparative Study of GA and ABC for Job Scheduling

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.