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 ...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.展开更多
A new prioritization method in the analytic hierarchy process （AHP）, which improves the group fuzzy preference programming （GFPP） method, is proposed. The fuzzy random theory is applied in the new prioritization m...A new prioritization method in the analytic hierarchy process （AHP）, which improves the group fuzzy preference programming （GFPP） method, is proposed. The fuzzy random theory is applied in the new prioritization method. By modifying the principle of decision making implied in the GFPP method, the improved group fuzzy preference programming （IGFPP） method is formulated as a fuzzy linear programming problem to maximize the average degree of the group satisfaction with all possible group priority vectors. The IGFPP method inherits the advantages of the GFPP method, and solves the weighting trouble existed in the GFPP method. Numerical tests indicate that the IGFPP method performs more effectively than the GFPP method in the case of very contradictive comparison judgments from decision makers.展开更多
This paper considers two-level integer programming problems involving random fuzzy variables with cooperative behavior of the decision makers. Considering the probabilities that the decision makers’ objective functio...This paper considers two-level integer programming problems involving random fuzzy variables with cooperative behavior of the decision makers. Considering the probabilities that the decision makers’ objective function values are smaller than or equal to target variables, fuzzy goals of the decision makers are introduced. Using the fractile criteria to optimize the target variables under the condition that the degrees of possibility with respect to the attained probabilities are greater than or equal to certain permissible levels, the original random fuzzy two-level integer programming problems are reduced to deterministic ones. Through the introduction of genetic algorithms with double strings for nonlinear integer programming problems, interactive fuzzy programming to derive a satisfactory solution for the decision maker at the upper level in consideration of the cooperative relation between decision makers is presented. An illustrative numerical example demonstrates the feasibility and efficiency of the proposed method.展开更多
基金supported by National High Technology Research and Development Program of China(863 Program)(No.2007AA041603)National Natural Science Foundation of China(No.60475035)+2 种基金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.)
文摘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.
基金Sponsored by the National Natural Science Foundation of China (70471063)
文摘A new prioritization method in the analytic hierarchy process （AHP）, which improves the group fuzzy preference programming （GFPP） method, is proposed. The fuzzy random theory is applied in the new prioritization method. By modifying the principle of decision making implied in the GFPP method, the improved group fuzzy preference programming （IGFPP） method is formulated as a fuzzy linear programming problem to maximize the average degree of the group satisfaction with all possible group priority vectors. The IGFPP method inherits the advantages of the GFPP method, and solves the weighting trouble existed in the GFPP method. Numerical tests indicate that the IGFPP method performs more effectively than the GFPP method in the case of very contradictive comparison judgments from decision makers.
文摘This paper considers two-level integer programming problems involving random fuzzy variables with cooperative behavior of the decision makers. Considering the probabilities that the decision makers’ objective function values are smaller than or equal to target variables, fuzzy goals of the decision makers are introduced. Using the fractile criteria to optimize the target variables under the condition that the degrees of possibility with respect to the attained probabilities are greater than or equal to certain permissible levels, the original random fuzzy two-level integer programming problems are reduced to deterministic ones. Through the introduction of genetic algorithms with double strings for nonlinear integer programming problems, interactive fuzzy programming to derive a satisfactory solution for the decision maker at the upper level in consideration of the cooperative relation between decision makers is presented. An illustrative numerical example demonstrates the feasibility and efficiency of the proposed method.
