Because calculation of stress intensity factors using the finite element method of the linear elastic fracture mechanics cannot satisfy the need for the real-time monitoring and the real-time analysis of cracks in concrete dams, a four-layer neural network for calculating stress intensity factors is proposed. The neural network is improved by chaos optimization algorithm. An example is given to validate the improvement.
In this paper, a new optimization method——chaos optimization combined with exact non-differentiable penalty function is proposed for solving chemical process optimization problems which are often regarded as nonlinear constraint optimization problems.
The paper advanced a new way to optimize the rolling parameters of cold continuous rolling mills is proposed by the use of mutative scale chaos optimization algorithm,chaotic search for the population of a genetic operation passed to overcome problems in convergence speed and local minima of Simple Genetic Algorithms. The way has the advantages:the fast search,the precise results,the convenient using ,suiting to on line calculation. With the example,the effectiveness of the method is proved.
Besides of researching, analyzing and controlling chaos, people are utilizing chaos in electric power systems too, e.g. chaos optimization of economic dispatch, parameter estimation of static load model, fuzzy power system stabilizer, and shortterm load forecasting.
This paper proposes a new search strategy using imitative scale chaos optimization algorithm (MSCO) for model selection of support vector machine (SVM).
Model selection for svm using imitative scale chaos optimization algorithm
The chaos optimization algorithm was used to help the gradient regularization method to escape from local optima in the hybrid algorithm.
Combining the chaos optimization algorithm with the gradient regularization method, a chaos-regularization hybrid algorithm was proposed to solve the established numerical model.
By the use of the properties of ergodicity, stochastic property, and"regularity" of chaos, a chaos optimization algorithms is proposed (COA). The efficiency of COA is much higher than some stochastic algorithms such asSAA and CA when COA is used to a kind of continuous problems. The chaos optimization method is very simple andconvenient to use.
In this paper, a new hybrid algorithm which combines the chaos optimization method and the conjugate gradient approach having an effective convergence property, is proposed. The hybrid algorithm can help the conjugate gradient approach to skip the local minimum. At the end, it can find the global minimum. The convergence of the algorithm is proved. The simulation shows that the hybrid algorithm is effective.
A mutative scale chaos optimization method is proposed based on the chaos variables. By continually reducing the searching space of variable optimized and enhancing the searching precision, the method is of high efficiency. Simulation results demonstrated the effectiveness of the algorithm.