粒子群算法在投影寻踪模型优化求解中的应用

Application of Particle Swarm Optimization for Solving Optimization Problem of Projection Pursuit Modeling

粒子群优化(Particle Swarm Optimization,PSO)算法是一种新兴的优化技术,其思想来源于人工生命和进化计算理论。PSO算法通过粒子追随自己找到的最好解和整个群体的最好解完成问题的优化。针对投影寻踪模型中的最佳投影方向优化问题,运用PSO算法和惩罚函数法相结合对该优化问题进行了计算。仿真实验结果表明:PSO算法对于求解有复杂约束的非线性目标函数优化问题是可行的,且算法的收敛速度快,编程结构简单,易于实现,从而为各领域运用投影寻踪模型评价方法提供了强有力的寻优方法,具有较广的应用前景。

PSO（ Particle Swarm Optimization）is a new optimization technique originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm. Aimed at the optimization problem of calculating the best projected vector of projection pursuit modeling, a typical example is given based on PSO. Numerical experiment has shown that it is feasible and effective to calculate the nonlinear function optimization problem. In addition, it can be programmed easily and widely used for other relative problems.