基于模拟退火的改进粒子群算法研究及应用

为加快粒子群算法效率,跳出局部最优陷阱,得到高精度最优解,文章提出了基于模拟退火的带收缩因子的粒子群混合算法(SACPSO)。首先,对混合优化算法进行了分析;然后,对混合算法进行函数数值仿真;最后,将SACPSO算法应用于PID参数整定问题。结果表明,改进粒子群算法的稳定性和搜索精度有了明显提高,收敛速度明显加快;在PID参数整定应用上,同传统方法相比,系统稳定,收敛性能好。

Research and Application of Improved PSO Algorithm Based on Simulated Annealing

In this paper, a new particle swarm optimization hybrid algorithm with constriction factors based on simulatedannealing was presented in order to speed up the efficiency of PSO algorithm and jump out of the local optimal trap andgain the best solutions. Firstly, the hybrid optimization algorithm was analyzed, then the numerical simulation of hybrid op.timization algorithm was carried out. Lastly, SACPSO algorithm was applied to the PID parameter tuning problem. The ex.perimental results showed that the accuracy, stability and convergence speed of SACPSO algorithm had improved obvious.ly. Compared with traditional methods, SACPSO algorithm had better stability and convergence in PID parameter tuningproblem.