A Parameter Adaptive Artificial Bee Colony Algorithm for Real-Parameter Optimization

Authors

  • Jian feng Qiu
  • Ji wen Wang
  • Dan Yang
  • Juan Xie
  • Nan zhen Yao

DOI:

https://doi.org/10.3991/ijoe.v9iS4.2609

Abstract


A new adaptive variant of Artificial Bee Colony algorithm, PAABC, is proposed to improve optimization performance and enhance the robustness of ABC algorithm by incorporating into searching strategy and updating control parameter of searching equation adaptively . The incorporation of the information is helpful to accelerate convergence while avoiding prematurity especially multimodal problem. According to the characteristic of optimization problem, the control parameter will be update adaptively. The better parameter value associated with the mean of the p% best individual will survive into the next generation. Experiment results show that PAABC is better or equal to evolutionary algorithm according to a set of basic test function and the CECâ??13 test suite.

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Published

2013-05-01

How to Cite

Qiu, J. feng, Wang, J. wen, Yang, D., Xie, J., & Yao, N. zhen. (2013). A Parameter Adaptive Artificial Bee Colony Algorithm for Real-Parameter Optimization. International Journal of Online and Biomedical Engineering (iJOE), 9(S4), pp. 34–39. https://doi.org/10.3991/ijoe.v9iS4.2609

Issue

Vol. 9 (2013): Special Issue: MEAMS2012 Conference

Section

Special Focus Papers

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