A Modified Fruit-Fly Optimization Algorithm aided PID controller designing

Abstract

Fruit Fly Optimization Algorithm (FOA) is one of the newest intelligent optimization algorithms. Attracted by its simple implement procedure with effective searching capability, our work is to popularize this algorithm to tackle some practical optimization applications requesting real-time performance. However, the updating strategy of FOA is with strong randomness, thus bringing in some blindness searching in solution updating, which will result in slow convergence rate and premature. Therefore, a modified FOA (MFOA) based on PSO and SA was proposed in this paper to improve the performance of basic FOA. Besides, Chaos function was used to enhance the stochastic and ergodic features of initial solution so as to improve the diversity of initial population in MFOA. PSO is introduced to reduce the blindness searching in solution updating. SA is used as a local search to improve the convergence rate. Finally, in order to verify the efficiency of MFOA algorithm, two common functions and a practical high-order AVR system with PID controller were tested in simulation. Experimental results revealed the encouraging performance of our proposed algorithm.