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Random Fuzzy Chance-constrained Programming Based on Adaptive Chaos Quantum Honey Bee Algorithm and Robustness Analysis

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International Journal of automation and computing 2010年第1期7卷 115-122页

作者： han xue xun li hong-xu ma college of electromechanical engineering and automation, national university of defense technology, changsha 410073, prc college of electromechanical engineering and automation National University of Defense Technology Changsha PRC

This paper proposes an adaptive chaos quantum honey bee algorithm （CQHBA） for solving chance-constrained program- ming 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 con- vergence 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.

关键词： Honey bee algorithm random fuzzy programming quantum computation chaos optimization robustness

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Swarm Intelligence Based WSN-Mediated Distributed Multi-Robot Task Allocation

Swarm Intelligence Based WSN-Mediated Distributed Multi-Robo...

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第二十七届中国控制会议

作者： xue han,Qin Haili,li xun,ma hongxu college of electromechanical engineering & automation,national university of defense technology,changsha 410073,P.R.China

To solve the multi-robot task allocation（MRTA） especially in unknown complex environment,a novel dynamic algorithm was put forward with the advantages of wireless sensor network（WSN） .With a powerful abstraction,a formal way of task representation was developed integrating four types of *** architecture and sensor fusion of WSN were *** colony optimization（ACO） algorithm based on bionic swarm intelligence was used for solution of the multi-objective *** pheromone updating strategy and deadlock elimination are included.A practical implementation with real WSN and real mobile robots were carried *** successful implementation of tasks validates the efficiency,stability and accuracy of the proposed *** convergence curve shows that as iterative generation grows,the utility increases and finally reaches a stable and optimal value,and that using sensor information fusion can greatly improve the *** algorithm is proved better than other tradition algorithms without WSN for MRTA in real time.

关键词： Adaptive Ant Colony Algorithm Multi-Robot Task Allocation Multi-Robot System Wireless Sensor Network Swarm Intelligence

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