An improved Fruit Fly Optimization Algorithm and its application in wet flue gas desulfurization system

Abstract

The strategy of the standard Fruit Fly Optimization Algorithm(FOA) is that all fruit flies search locally and randomly with the optimal solution, it is quite easy to get into local optimum; Meanwhile, as the step size is fixed, it cannot achieve high accuracy. In order to enhance the algorithm's ability to avoid falling into local optimum and improve the accuracy, this paper uses different tactics to deal with the flight strategy and step size of fruit flies by comparing the excellent degree of candidate solutions; Furthermore, by adding the variance threshold of concentration value, adopting the linear generation mechanism of candidate solutions, we propose a Dynamic Step Size Fruit Fly Optimization Algorithm(DSS-FOA) based on candidate solutions. Several standard test functions are used to test, results of the test confirm that DSS-FOA is superior to the standard FOA in global optimization, local optimization, accuracy and stability. Besides, the algorithm is applied to the PID parameters' optimization of pH in wet flue gas desulfurization(FGD) system, the results of simulation show that the algorithm has excellent effect in the PID parameters' optimization of pH in wet FGD system, and performs much better than the standard FOA.