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Dynamic Swarm Artificial Bee Colony Algorithm

Author

Listed:
  • Harish Sharma

    (ABV-Indian Institute of Information Technology and Management, Gwalior, India)

  • Jagdish Chand Bansal

    (ABV-Indian Institute of Information Technology and Management, Gwalior, India)

  • K. V. Arya

    (ABV-Indian Institute of Information Technology and Management, Gwalior, India)

  • Kusum Deep

    (Indian Institute of Technology, Roorkee, Roorkee, India)

Abstract

Artificial Bee Colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over test problems as well as real world optimization problems. This paper presents an attempt to modify ABC to make it less susceptible to stick at local optima and computationally efficient. In the case of local convergence, addition of some external potential solutions may help the swarm to get out of the local valley and if the algorithm is taking too much time to converge then deletion of some swarm members may help to speed up the convergence. Therefore, in this paper a dynamic swarm size strategy in ABC is proposed. The proposed strategy is named as Dynamic Swarm Artificial Bee Colony algorithm (DSABC). To show the performance of DSABC, it is tested over 16 global optimization problems of different complexities and a popular real world optimization problem namely Lennard-Jones potential energy minimization problem. The simulation results show that the proposed strategies outperformed than the basic ABC and three recent variants of ABC, namely, the Gbest-Guided ABC, Best-So-Far ABC and Modified ABC.

Suggested Citation

  • Harish Sharma & Jagdish Chand Bansal & K. V. Arya & Kusum Deep, 2012. "Dynamic Swarm Artificial Bee Colony Algorithm," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 3(4), pages 19-33, October.
    • Handle: RePEc:igg:jaec00:v:3:y:2012:i:4:p:19-33
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