改进PSO算法在氧化动力学模型参数估计中的应用

通过构造一个适当的目标函数,将Hg氧化动力学模型的参数估计问题转化为一个多维数值优化问题;然后提出一种基于算术交叉和多样性变异的改进PSO算法来求解该优化问题。算法随机选择粒子与当前最优粒子进行算术交叉操作,将粒子逐步向极值点引导,提高算法的局部搜索能力。引入多样性变异算子以维持种群粒子的多样性。几个标准测试函数的实验结果表明算法具有较好的寻优性能。将算法应用于Hg氧化动力学模型参数估计中,获得了满意的结果。

Application of modified particle swarm optimization algorithm in parameter estimation for oxidation kinetics model

Through establishing an appropriate objective function, the parameter estimation problem for Hg oxidation kinetic model was formulated as a multi-dimensional numerical optimization problem, which can be solved by modified particle swarm optimization (CMPSO) algorithm. In the evolution process, arithmetic crossover operator is utilized for the randomly selected particle and the optimal particle. The crossover operator can lead gradually the population to the extreme point and improve the local searching ability. In addition, diversity mutation strategy is introduced to maintain the diversity of the particle. Several benchmark functions are tested; the experimental results show that CMPSO has better optimization performance. CMPSO is applied to estimate the kinetic parameters of Hg oxidation, and a satisfactory result is obtained.