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Random fuzzy chance-constrained programming based on adaptive chaos quantum honey bee algorithm and robustness analysis

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Abstract

This paper proposes an adaptive chaos quantum honey bee algorithm (CQHBA) for solving chance-constrained programming in random fuzzy environment based on random fuzzy simulations. Random fuzzy simulation is designed to estimate the chance of a random fuzzy event and the optimistic value to a random fuzzy variable. In CQHBA, each bee carries a group of quantum bits representing a solution. Chaos optimization searches space around the selected best-so-far food source. In the marriage process, random interferential discrete quantum crossover is done between selected drones and the queen. Gaussian quantum mutation is used to keep the diversity of whole population. New methods of computing quantum rotation angles are designed based on grads. A proof of convergence for CQHBA is developed and a theoretical analysis of the computational overhead for the algorithm is presented. Numerical examples are presented to demonstrate its superiority in robustness and stability, efficiency of computational complexity, success rate, and accuracy of solution quality. CQHBA is manifested to be highly robust under various conditions and capable of handling most random fuzzy programmings with any parameter settings, variable initializations, system tolerance and confidence level, perturbations, and noises.

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Authors and Affiliations

  1. College of Electromechanical Engineering and Automation, National University of Defense Technology, Changsha, 410073, PRC

    Han Xue, Xun Li & Hong-Xu Ma

Authors
  1. Han Xue
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  2. Xun Li
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  3. Hong-Xu Ma
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Corresponding author

Correspondence to Han Xue.

Additional information

This work was supported by National High Technology Research and Development Program of China (863 Program) (No. 2007AA041603), National Natural Science Foundation of China (No. 60475035); Key Technologies Research and Development Program Foundation of Hunan Province of China (No. 2007FJ1806); Science and Technology Research Plan of National University of Defense Technology (No.CX07-03-01), Top Class Graduate Student Innovation Sustentation Fund of National University of Defense Technology (No.B070302.)

Han Xue received the M. Sc. degree from National University of Defense Technology, PRC in 2007. She is currently a Ph.D. candidate at the College of Electromechanical Engineering and Automation, National University of Defense Technology.

Her research interests include robotics and automation, especially the swarm intelligence.

Xun Li received the Ph.D. degree from National University of Defense Technology, PRC in 2001. He is currently an associate professor at the College of Electromechanical Engineering and Automation, National University of Defense Technology.

His research interest includes embedded system, especially the wireless sensor networks.

Hong-Xu Ma received the Ph.D. degree from National University of Defense Technology, PRC in 1995. He is currently a professor at the College of Electromechanical Engineering and Automation, National University of Defense Technology.

His research interests include robotics and automation, especially the humanoid control.

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Xue, H., Li, X. & Ma, HX. Random fuzzy chance-constrained programming based on adaptive chaos quantum honey bee algorithm and robustness analysis. Int. J. Autom. Comput. 7, 115–122 (2010). https://doi.org/10.1007/s11633-010-0115-6

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  • DOI: https://doi.org/10.1007/s11633-010-0115-6

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