求解高维函数优化的动态粒子群算法

Dynamic particle swarm optimization for solving high dimensional function

针对基本粒子群优化算法对高维函数优化时搜索精度不高的缺陷,提出了一种动态粒子群优化算法。该算法采用了通过调节阈值对粒子运动轨迹进行动态改变的策略,使得粒子对周围环境的适应能力不受进化代数的影响,从而保证了算法在迭代后期仍具有较强的搜索能力。实验结果表明,与文献算法相比,该算法在处理高维函数优化时具有更强的寻优能力和更高的搜索精度。

To improve the search quality of the standard PSO algorithm for solving high-dimensional function,a dynamic particle swarm optimization algorithm is proposed.The strategy that particle trajectory is changed dynamically by adjusting the threshold value is used to make particles adaptability for the surrounding environment without the influence of evolutionary algebra,and the strong search capability of algorithm in iterative later is ensured.Simulations show that proposed algorithm has more powerful optimizing ability and higher optimizing precision in high-dimensional function optimization than literature algorithms.