针对小生境粒子群优化技术中小生境半径等参数选取问题，提出了一种新颖的小生境方法，无须小生

境半径等任何参数。通过监视粒子正切函数值的变化，判断各个粒子是否属于同一座山峰，使其追踪所在山峰

的最优粒子飞行，进而搜索到每一座山峰极值。算法实现简单，不仅克服了小生境使用中需要参数的弊端，而且

解决了粒子群算法只能找到一个解的不足。最后通过对多峰值函数的仿真实验，验证了算法可以准确地找到所

有山峰-Proposed a novel niche for niche particle swarm optimization technology niche radius parameter selection problem, without niche habitat radius of any parameter. By monitoring changes in the value of the particle tangent function to determine whether the individual particles belong with a mountain to make it the optimal particle track where the mountain flight, and then searched every hill extremum. The algorithm is simple, not only to overcome the drawbacks of the need to use the niche parameters, and solve the particle swarm algorithm can only find a solution to the lack of. Finally, through the simulation of multi-peak function to verify the algorithm can accurately find all the peaks