基于适应度反馈作用的PSO算法改进

粒子群优化算法的收敛速度较慢、精度较低、稳定性欠佳。为此，提出一种基于适应度反馈作用的改进粒子群优化算法。在运行过程中，根据粒子相邻2次迭代的适应度变化，对适应度变化值归一化处理后，将其反馈给惯性权重，以削弱粒子寻优过程中的适应度振荡幅度，增强粒子群跳出局部最优的能力。测试结果表明，该算法的全局搜索能力得到提高，具有较高的收敛速度和稳定性。

Improvement of PSO Algorithm Based on Fitness Feedback Effect

According to the low convergence speed, poor accuracy and stability of Particle Swarm Optimization(PSO) algorithm, an improved PSO algorithm based on fitness feedback effect is proposed in this paper. By calculating and normalizing the variances of fitness in the neighboring iterations and then imposing feedback on inertial weights, the fitness oscillation is weakened and the ability of avoiding local-optimization is enhanced during the optimizing process. Test results show that the global search ability is improved, and this algorithm has high convergence speed and stability.