Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model ? chaotic parameters disturbance annealing (CPDA) network, which is superior to ...Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model ? chaotic parameters disturbance annealing (CPDA) network, which is superior to other existing neural networks, genetic algorithms, and simulated annealing algorithms in global optimization. In the present CPDA network, we add some chaotic parameters in the energy function, which make the Hopfield neural network escape from the attraction of a local minimal solution and with the parameter annealing, our model will converge to the global optimal solutions quickly and steadily. The converge ability and other characters are also analyzed in this paper. The benchmark examples show the present CPDA neural network's merits in nonlinear global optimization.展开更多
To deal with the demerits of constriction particle swarm optimization(CPSO), such as relapsing into local optima, slow convergence velocity, a modified CPSO algorithm is proposed by improving the velocity update formu...To deal with the demerits of constriction particle swarm optimization(CPSO), such as relapsing into local optima, slow convergence velocity, a modified CPSO algorithm is proposed by improving the velocity update formula of CPSO. The random velocity operator from local optima to global optima is added into the velocity update formula of CPSO to accelerate the convergence speed of the particles to the global optima and reduce the likelihood of being trapped into local optima. Finally the convergence of the algorithm is verified by calculation examples.展开更多
An optimization model is constructed to formulate the maximization problem on the capacity of V-belt drive. The concavity,the monotonicity and the global optimality condition are studied for the objective function,and...An optimization model is constructed to formulate the maximization problem on the capacity of V-belt drive. The concavity,the monotonicity and the global optimality condition are studied for the objective function,and it is proved that the feasible region of the model is bounded,closed and convex under some design conditions. Then,a solution method,called an optimal segment algorithm,is developed to find the global maximizer of the model. Under four different design conditions,solution methods are presented respectively. Some real case studies are employed to demonstrate that the model and the algorithm in this paper are promising.展开更多
文摘Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model ? chaotic parameters disturbance annealing (CPDA) network, which is superior to other existing neural networks, genetic algorithms, and simulated annealing algorithms in global optimization. In the present CPDA network, we add some chaotic parameters in the energy function, which make the Hopfield neural network escape from the attraction of a local minimal solution and with the parameter annealing, our model will converge to the global optimal solutions quickly and steadily. The converge ability and other characters are also analyzed in this paper. The benchmark examples show the present CPDA neural network's merits in nonlinear global optimization.
基金supported by the National Natural Science Foundation of China(71171015)the National High Technology Research and Development Program(863 Program)(2012AA112403)
文摘To deal with the demerits of constriction particle swarm optimization(CPSO), such as relapsing into local optima, slow convergence velocity, a modified CPSO algorithm is proposed by improving the velocity update formula of CPSO. The random velocity operator from local optima to global optima is added into the velocity update formula of CPSO to accelerate the convergence speed of the particles to the global optima and reduce the likelihood of being trapped into local optima. Finally the convergence of the algorithm is verified by calculation examples.
基金supported by the National Natural Science Foundation of China (Grant Nos.71071162,70921001)the project for Excellent Talent of New Century,Ministry of Education of China (Grant No.NCET-07-0864)
文摘An optimization model is constructed to formulate the maximization problem on the capacity of V-belt drive. The concavity,the monotonicity and the global optimality condition are studied for the objective function,and it is proved that the feasible region of the model is bounded,closed and convex under some design conditions. Then,a solution method,called an optimal segment algorithm,is developed to find the global maximizer of the model. Under four different design conditions,solution methods are presented respectively. Some real case studies are employed to demonstrate that the model and the algorithm in this paper are promising.
