CHAOTIC ANNEALING NEURAL NETWORK FOR GLOBAL OPTIMIZATION OF CONSTRAINED NONLINEAR PROGRAMMING

用于约束非线性全局优化的混沌退火神经网络(英文)

Chaotic neural networks have global searching ability.But their applications are generally confined to combinatorial optimization to date.By introducing chaotic noise annealing process into conventional Hopfield network,this paper proposes a new chaotic annealing neural network (CANN) for global optimization of continuous constrained non linear programming.It is easy to implement,conceptually simple,and generally applicable.Numerical experiments on severe test functions manifest that CANN is efficient and reliable to search for global optimum and outperforms the existing genetic algorithm GAMAS for the same purpose.

混沌神经网络具有全局搜索能力 ,但其运用至今主要局限于组合优化 .通过对普通 Hopfield优化网络引入混沌噪声退火过程 ,提出了一种用于约束非线性全局优化的混沌退火神经网络 ,它易于实现 ,原理简明 ,应用广泛 .对很复杂的测试函数的数字试验表明 ,该模型能够高效、可靠地搜索到全局最优 ,其性能超过遗传算法 GAMA