改进约束鸡群算法在神经网络的应用

Application of an improved constrained chicken swarm optimization algorithm in neural networks

针对基本鸡群优化算法CSO存在收敛速度慢、易陷入局部最优等问题,提出一种改进约束鸡群算法ICCSO,改进了基本鸡群算法的边界约束处理机制,提高了算法的收敛速度和全局搜索能力。以标准测试函数和BP神经网络为例进行数值仿真,仿真结果表明了所提出的改进约束鸡群优化算法的合理性及有效性。

The basic chicken swarm optimization algorithm(CSO)is of slow convergence and easy to fall into local optimum.Aiming at the problems,we propose an improved constrained chicken swarm optimization algorithm(ICCSO)to improve the boundary constraint processing mechanism of the basic chicken swarm optimization,and the convergence speed and global search ability of the algorithm are also improved.Standard test functions and BP neural networks are taken as examples to demonstrate the rationality and effectiveness of the proposed algorithm.