Author(s): Manish Gupta | Govind sharma
Journal: International Journal of Soft Computing & Engineering
ISSN 2231-2307
Volume: 1;
Issue: 6;
Start page: 291;
Date: 2012;
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Keywords: Artificial Bee Colony | ABC | Genetic Algorithm | GA | Mutation | crossover.
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.
Journal: International Journal of Soft Computing & Engineering
ISSN 2231-2307
Volume: 1;
Issue: 6;
Start page: 291;
Date: 2012;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: Artificial Bee Colony | ABC | Genetic Algorithm | GA | Mutation | crossover.
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.