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

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 rando...

Full description

Saved in:
Bibliographic Details
Published in:International journal of automation and computing 2010-02, Vol.7 (1), p.115-122
Main Authors: Xue, Han, Li, Xun, Ma, Hong-Xu
Format: Article
Language:English
Subjects:
Algorithms
CAE) and Design
Chaos theory
Computation
Computer Applications
Computer-Aided Engineering (CAD
Control
Engineering
Fuzzy
Fuzzy logic
Fuzzy set theory
Mathematical models
Mechatronics
Programming
Robotics
模糊机会约束规划
混沌优化
算法
自适应
蜜蜂
量子比特
随机模糊
鲁棒性分析
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1476-8186
1751-8520
DOI:10.1007/s11633-010-0115-6