一种改进的粒子群优化算法
An improved particle swarm optimization algorithm
云南民族大学学报:自然科学版,2017,26(1):60-63

封京梅 FJM

摘要


针对粒子群优化算法在迭代后期容易陷入局部最优、收敛速度变慢,精度降低、计算效率变差等缺点,提出了一种改进的粒子群优化算法.此算法通过引入惯性权重来调节粒子的速度变化,动态变化的学习因子来平衡粒子的社会学习能力和自我学习能力.通过测试函数检验,结果显示该算法能够有效摆脱局部最优,整个收敛速度明显变快,精度大幅提高. Because the particle swarm optimization algorithm is easy to fall into a local optimum, slower convergence, lower precision and poor calculation efficiency in the later iterations, the paper presents an improved particle swarm optimization algorithm to adjust the speed of particles through inertia weight and to balance the social learning ability and the self-learning ability of particles by the dynamic learning factor. Through the test of function simulation, the results show that this algorithm gets rid of the local optimum, and effectively improves the rate of convergence and accuracy.

参考



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