自适应决策粒子群的相关搜索优化

摘要

在传统粒子群相关搜索算法(PSO)基础上提出一种新的数字相关算法，自适应决策粒子群优化(ADI-PSO)相关搜索算法。ADI-PSO综合了自适应惯性因子和决策终止判定两种特点，使得该算法能够通过在迭代过程中不断更新惯性因子加快收敛速度，同时能够智能判定终止与否，因而能更快的获取准确的搜索结果。通过两组对比试验验证了ADI-PSO算法的精度相对N-R算法接近一致，但搜索速度更快；粗糙散斑图下三种不同粒子群方法得到最佳适应度均值为-0.0386、0.8881、0.9176，相较于传统PSO、LDI-PSO，ADI-PSO运算时间均值最少，稳定性更高，收敛速度更快，并能有效避免过早收敛。%Based on the traditional particle swarm search algorithm, a new search algorithm is proposed, which is called Adaptive Decision Inertia weight Particle Swarm Optimization (ADI-PSO) correlation searching algorithm. ADI-PSO combines adaptive inertia factor and policy termination, which can continually update inertia factor to accelerate the convergence speed during the iterations. The algorithm can intelligently determine whether to end the iteration, so it can get accurate search results more quickly. Two groups of contrast experiments show that the precision of ADI-PSO algorithm and N-R algorithm are much the same, but the search speed of ADI-PSO is more quickly. In the case of rough speckle image, the optimal fitness value is -0.038 6, 0.888 1, 0.917 6 respectively using three different methods. Compared with PSO and LDI-PSO, the convergence speed of ADI-PSO is more quickly, and the stability is better. Besides, ADI-PSO can overcome premature convergence effectively.