基于模拟退火机制的自适应粘性粒子群算法

Adaptive stickiness particle swarm optimization algorithm based on simulated annealing mechanism

为了进一步提升粒子群算法在离散优化问题中的性能,针对粘性二进制粒子群算法缺乏全局搜索能力、容易陷入局部最优和收敛速度慢的缺点,提出一种新的自适应参数策略和粒子散度指标,并结合模拟退火机制改善该算法的寻优能力.为了检验算法性能,通过选取不同维数的背包问题算例库以及不同规模的UCI特征选择问题算例库进行仿真实验,并对实验数据进行统计分析.实验以及分析结果表明,所提算法在寻优精度、算法稳定性和收敛速度上均优于对比算法.

In order to further improve the performance of particle swarm optimization in discrete optimization problems,aiming at the shortcomings of stickiness binary particle swarm optimization,such as lack of global search ability,easy to fall into local optimum and slow convergence speed,a new adaptive parameter strategy and a particle divergence index are proposed,which are combined with simulated annealing mechanism to improve the optimization ability of the algorithm.In order to test the performance of the algorithm,simulation experiments are carried out by selecting the knapsack problem case library with different dimensions and the UCI feature selection problem case library with different scales,and the experimental data are statistically analyzed.The experimental and analytical results show that the proposed algorithm is superior to the comparison algorithm in optimization accuracy,algorithm stability and convergence speed.