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Discrete and Continuous Optimization Based on Hierarchical Artificial Bee Colony Optimizer

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Listed:
  • Lianbo Ma
  • Kunyuan Hu
  • Yunlong Zhu
  • Ben Niu
  • Hanning Chen
  • Maowei He

Abstract

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), to tackle complex high‐dimensional problems. In the proposed multilevel model, the higher‐level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part‐dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Experiments are conducted on a set of 20 continuous and discrete benchmark problems. The experimental results demonstrate remarkable performance of the HABC algorithm when compared with other six evolutionary algorithms.

Suggested Citation

  • Lianbo Ma & Kunyuan Hu & Yunlong Zhu & Ben Niu & Hanning Chen & Maowei He, 2014. "Discrete and Continuous Optimization Based on Hierarchical Artificial Bee Colony Optimizer," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:402616
    DOI: 10.1155/2014/402616
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    References listed on IDEAS

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    1. David H. Wolpert & William G. Macready, 1995. "No Free Lunch Theorems for Search," Working Papers 95-02-010, Santa Fe Institute.
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