Novel Reverse Driving based Fruit Fly Optimization Algorithm for Parameter Optimization

Abstract

Traditional swarm intelligence based parameter optimization algorithms always have the problems of high computational complexity, strong parameter dependence and poor global searching ability. On this basis, a reverse driving based fruit fly optimization algorithm is proposed. The concept of worst group is proposed and the single "attractant" based attraction operation is used to update the positions of the fruit flies. Moreover, a worst group based reverse driving operation is proposed to solve the problem of local optimum. Experimental results on four standard functions show that, the convergence accuracy of the proposed method is improved when compared to traditional swarm intelligence based optimization algorithms.