改进的粒子群算法在烧结配料中的应用

为提高计算机烧结配料的自适应性和通用性,提出了基于改进粒子群算法优化求解的方法。该方法采用柯西分布函数演化而来的调整函数,根据迭代次数惯性权重动态调整,对粒子群算法的全局和局部搜索能力进行平衡调整,使算法初期有较快的收敛速度,后期又保持较高的寻优精度,从而提高了粒子群算法的全局和局部搜索能力。仿真结果表明,所提出的改进粒子群算法收敛速度快、精度高、具有较强的全局寻优能力,能有效降低钢铁企业烧结成本,为实际工程应用提供了一个新思路。

Improved particle swarm optimization algorithm and application to sintering blending

In order to improve the computer sintering ingredients adaptability and versatility, an optimized solution based on particle swarm algorithm was proposed. The method introduces the adjustment function which was evolved by Cauchy distribution function, dynamically adjustment inertia weight according to the number of iterations and improves the global and local search ability of particle swarm algorithm by balance adjustment on the global and local search ability of particle swarm optimization in the iterative process. The simulation results show that the proposed of the particle swarm algorithm have fast convergence speed and high precision, and have strong ability of global optimization, which can effectively reduce the sintering cost. and provide a new way of thinking for the engineering application.