连续变化目标下基于外部邻域位置学习的粒子群优化算法

Particle swarm optimization algorithm based on imitating external neighborhood location under continuously changing targets

为提高粒子群算法在面对连续变化目标的多次求解问题时的收敛速度,提出基于外部邻域位置学习的粒子群算法(IENLPSO)。通过在粒子群算法中加入对外部邻域位置的学习,使下一状态的求解过程更具有目标性。以基准测试函数及IEEE 33节点系统为例进行仿真测试,结果表明,相比于传统粒子群算法,提出的算法在减少迭代次数的同时,进一步提高了算法的求解精度。

In order to improve the convergence speed of the particle swarm algorithm in the face of the continuously changing targets for the multiple solving problems,a particle swarm optimization algorithm based on the imitating external neighborhood location(IENLPSO)is proposed.By adding the learning of the external neighborhood position in the particle swarm algorithm,the convergence speed and accuracy of the algorithm are accelerated.The typical test functions are used to test,and the results verify the effectiveness of the improved algorithm.