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A Modified Artificial Bee Colony Algorithm with Firefly Algorithm Strategy for Continuous Optimization Problems

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  • Amnat Panniem
  • Pikul Puphasuk

Abstract

Artificial Bee Colony (ABC) algorithm is one of the efficient nature‐inspired optimization algorithms for solving continuous problems. It has no sensitive control parameters and has been shown to be competitive with other well‐known algorithms. However, the slow convergence, premature convergence, and being trapped within the local solutions may occur during the search. In this paper, we propose a new Modified Artificial Bee Colony (MABC) algorithm to overcome these problems. All phases of ABC are determined for improving the exploration and exploitation processes. We use a new search equation in employed bee phase, increase the probabilities for onlooker bees to find better positions, and replace some worst positions by the new ones in onlooker bee phase. Moreover, we use the Firefly algorithm strategy to generate a new position replacing an unupdated position in scout bee phase. Its performance is tested on selected benchmark functions. Experimental results show that MABC is more effective than ABC and some other modifications of ABC.

Suggested Citation

  • Amnat Panniem & Pikul Puphasuk, 2018. "A Modified Artificial Bee Colony Algorithm with Firefly Algorithm Strategy for Continuous Optimization Problems," Journal of Applied Mathematics, John Wiley & Sons, vol. 2018(1).
    • Handle: RePEc:wly:jnljam:v:2018:y:2018:i:1:n:1237823
    DOI: 10.1155/2018/1237823
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    References listed on IDEAS

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    1. Zhen-an He & Caiwen Ma & Xianhong Wang & Lei Li & Ying Wang & Yuan Zhao & Huinan Guo, 2014. "A Modified Artificial Bee Colony Algorithm Based on Search Space Division and Disruptive Selection Strategy," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-14, September.
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