一种用于PID控制参数优化的混合果蝇算法

针对果蝇优化算法（ FOA）收敛速度快但寻优精度低的缺点，为了改善果蝇算法的优化性能，提出一种混合果蝇优化算法（ HFOA）。HFOA采用分段优化的思想，在优化过程后期采用收敛稳定性较好的粒子群优化（ PSO）算法优化果蝇算法中果蝇个体飞行距离和味道浓度的判定值，采用误差性能指标积分准则ITAE作为适应度函数，并将优化方案应用于一类不稳定系统的PID控制。Matlab仿真验证表明：HFOA计算高效，具有良好的稳定性，收敛精度高，进而验证了HFOA应用于PID控制参数优化是可行而有效的。

A hybrid fly fruit algorithm for PID control parameters optimization

In order to improve optimization performance of fly fruit optimization algorithm( FOA),put forward an assemblage of subsection optimization of a hybrid fly fruit optimization algorithm( hybrid FOA,HFOA),which introduces steady particle swarm optimization( PSO )algorithm into FOA parameters optimization of individual flying distance and smell concentration judgement value,meanwhile,the hybrid method adopts ITAE as fitness function and is applied to a class of unstable systems in PID control. Matlab simulation verification show that HFOA algorithm has fast convergence,good stability and high precision,and it verifies that application of HFOA in PID control parameter optimization are feasible and effective.