基于粒子群算法的温度模型优化

PSO Based Temperature Model Optimization

粒子群算法是一种新出现的进化算法,相对其它进化算法,它收敛速度快、规则简单、编程易于实现.现实的优化问题一般要求在指定的范围内求解,即要满足域约束.而目前在粒子群的约束优化问题上面研究较少.本文对粒子群算法的种群初始化进行了改进,在指定范围内进行初始化;并且提出了一种解决域约束问题的方法.该方法在优化温度模型的应用中取得了比较好的效果.

Particle swarm optimization algorithm is a new developed evolutionary algorithm.Comparing with other evolutonary algorithms,it converges more quickly and its rules are more simple,also the programming is more easy.Practical problem always demands that the solutions must be in specified region,that is regioin constrain.Few researchers focus on the constrained optimization of PSO.This article modified the initialization approch of PSO Algorithm,initializing the particle swarm in specified region instead;it also developed a new approach to solve the region constrained optimization problem.These approaches worked very well when used to optimize the temperature model.