【摘要】 粒子群算法在解决多维的复杂优化问题时,存在收敛精度不高和易陷入局部收敛等不足,针对这些问题,将莱维飞行与偏好随机游动引入粒子群算法中,提出莱维飞行与粒子群的混合搜索算法。在该算法的解更新过程中,采用莱维飞行、偏好随机游动与粒子群算法的更新方程以串行方式对得到的解进行更新寻优。实验结果表明,改进后的混合算法与粒子群算法相比较,加快了收敛速度,提高了搜索精度。更多还原

【Abstract】 Particle swarm optimization algorithm has some weak points to solve multi-dimensional and complex optimization problems. Its convergence precision is not high enough,and it is easy to fall into local convergence. In order to overcome these problems,Levy flight and preference of rand walk are applied in the basic particle swarm optimization algorithm. The main method is to incorporate the updating equation of particle swarm optimization algorithm with levy flight and preference of rand walk in a serial fashion. The experimental results demonstrate that the improved algorithm has considerable advantages in search accuracy and the convergence speed while comparing with the basic particle swarm algorithm.更多还原