This paper presents an improved fast simulated annealing algorithm based on the analysis on deterministic method and simulated annealing algorithm for solving global optimization problems.
基于对求解全局优化问题的确定性方法和模拟退火算法的分析，文中提出了一种改进的快速模拟退火算法。
Through embedding a gradient descend operator into the generic algorithm, a hybrid algorithm is achieved with fast convergence and great probability for global optimization.
在遗传算法中嵌入一个梯度下降算子，使得混合算法既有较快的收敛性，又能以较大概率得到全局极值。
Through simulation and large, the algorithm shown in a complex nonlinear optimization is fast, efficient, robust features of the strong, and the global scope effective search all the optimal solution.
通过大量仿真和比较，表明算法在复杂非线性优选中具有快速、高效、鲁棒性强的特点，并能在全局范围内有效搜索所有最优解。
A fast stochastic global optimization algorithm, particle group optimization algorithm, was used for training the fuzzy neural network.
模糊神经网络的学习算法采用的是快速的粒子群优化算法。
The proposed adaptive optimization method has been validated using several analytic function tests. Numerical results show that the algorithm has the property of fast global searching.
利用解析函数对上述自适应优化方法进行了验证，算例结果证明了该算法的全局搜索和快速寻优能力。
Advantages: fast convergence with global optimization capability and programming simple and easy to use.
优点：收敛速度快，具有全局寻优能力，而且编程简单，易于推广使用。
Advantages: fast convergence with global optimization capability and programming simple and easy to use.
优点：收敛速度快，具有全局寻优能力，而且编程简单，易于推广使用。
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