Aiming at problem that Particle Swarm Optimization (PSO) algorithm falls into local optimum easily, this paper presents a PSO algorithm based on sub-region.
针对粒子群优化(PSO)算法在寻优时容易陷入局部最优的不足，提出一种基于子区域的PSO算法。
I know that, going into a home-based franchise model, it falls on the franchisee to grow the business in the local territory assigned.
我知道，进入一个以家庭为基础的特许经营模式，其加盟业务的增长与其所在的指定地段息息相关。
But the result easily falls into the local optimum with random initial choice, and more control points are required to assure higher accuracy.
但随机选取初始种群的遗传算法，容易使得结果陷入局部最优。要达到较高的拟合精度，则需要增加更多的控制顶点。
But the result easily falls into the local optimum with random initial choice, and more control points are required to assure higher accuracy.
但随机选取初始种群的遗传算法，容易使得结果陷入局部最优。要达到较高的拟合精度，则需要增加更多的控制顶点。
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