一种自适应模拟退火粒子群优化算法

Adaptive simulated annealing particle swarm optimization algorithm

为了提高粒子群算法的寻优速度和精度,避免陷入局部优解,提出一种自适应模拟退火粒子群优化算法。采用双曲正切函数来控制惯性权重系数,进行非线性自适应变化;利用线性变化策略控制社会学习因子和自我学习因子,达到改变不同阶段寻优重点的目的;引入模拟退火操作,根据种群的初始状态设置一个温度,根据米特罗波利斯准则和温度指导种群以一定的概率接受差解,保证了算法跳出局部最优解的能力。为验证这种算法的效果,选择7种典型测试函数与已有文献中提出的5种粒子优化算法进行对比实验,根据寻优结果的平均值、标准差以及迭代次数等数据,证明文中所提算法在迭代精度、收敛速度以及稳定性上都有很大的提升,有效地弥补了经典粒子群算法的缺陷。

Particle swarm optimization is widely used in various fields because of the few parameters to be set and the simple calculation structure.In order to improve the optimization speed and accuracy of the PSO,and to avoid falling into the local optimal solution,an adaptive simulated annealing PSO is proposed,which uses the hyperbolic tangent function to control the inertia weight factor for nonlinear adaptive changes,uses linear change strategies to control 2 learning factors,introduces the simulation annealing operation,set a temperature according to the initial state of the population,guide the population to accept the difference solution with a certain probability according to the Metropolis criterion,and ensure the ability to jump out of the local optimal solution.To verify the effect of the algorithm proposed in this paper,7 typical test functions and 5 algorithms proposed in the literature are selected for comparison and testing.According to the average value,standard deviation and number of iterations of the optimization results,the algorithm proposed in this paper has greatly improved the iteration accuracy,convergence speed and stability so as to overcome the shortcomings of particle swarm optimization.