求解非线性方程组方法有经典算法以及近年来流行的遗传算法．牛顿法及其改进形式,但是此类算法的收敛性在很大程度上依赖于初始点的选择，对于某些非线性方程组容易导致求解失败 为了克服经典算法的缺点，设计了求解非线性方程组的混合遗传算法,但依然对方程组和编码方法有很高要求。PSO是受到鸟群或者鱼群社会行为的启发而形成的一种基于种群的随机优化技术。它是一类随机全局优化技术，通过粒子间的相互作用发现复杂搜索空间中的最优区域。该算法是一种基于群体智能的新型演化计算技术，具有简单易实现、设置参数少、全局优化能力强等优点。粒子群优化算法已在函数优化、神经网络设计、分类、模式识别、信号处理、机器人技术等许多领域取得了成功的应用。 本函数包带函数，测试函数与使用简介-Methods for solving nonlinear equations and the recent popular classical algorithm genetic algorithm. Newton　s method and its improved form, but such convergence of the algorithm is largely dependent on the choice of the initial point, for some nonlinear equations solved easily lead to failure　In order to overcome the shortcomings of classical algorithms designed for solving nonlinear Equations hybrid genetic algorithm, but still on the equations and the encoding method has high requirements. PSO is social behavior by birds or fish inspired the formation of a population-based stochastic optimization techniques. It is a class of stochastic global optimization technique, through complex interaction between particles found in the search space optimum area. The algorithm is a novel based on swarm intelligence evolutionary computation techniques, with a simple and easy to achieve, set few parameters, global optimization ability, etc.. Particle swarm optimization in function optimizatio