Comparative Study of Different Modified Artificial Bee Colony Algorithm with Proposed ABC Algorithm
Vani Maheshwari
Vani Maheshwari Maharana Pratap College of Technology, Gwalior (M.P.)-India
Manuscript received on December 08, 2014. | Revised Manuscript received on December 15, 2014. | Manuscript published on January 05, 2014. | PP: 150-153 | Volume-3 Issue-6, January 2014. | Retrieval Number: E1909113513/2014©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: Swarm intelligence systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. Artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE) etc, are some example of swarm intelligence. In this work, an efficient modified version of ABC algorithm is proposed, where two additional operator crossover and mutation operator is used in the standard artificial bee colony algorithm. Here Crossover operator is used after the employed bee phase and mutation operator is used after scout bee phase of ABC algorithm and simulated results are compared with different modified version of artificial bee colony algorithms, like ABC with uniform mutation, ABC with crossover and mutation and Basic ABC algorithm. The simulated result showed that the proposed algorithm is better than all the modified version of ABC algorithm.
Keywords: Artificial Bee Colony, ABC, crossover, Mutation, Genetic Algorithm, GA..