A novel phase angle-encoded fruit fly optimization algorithm with mutation adaptation mechanism applied to UAV path planning

Abstract

This paper proposed an improved version of fruit fly optimization (FOA) to solve the unmanned aerial vehicle (UAV) path planning problem. The improved algorithm combines the phase angle-encoded and mutation adaptation mechanisms into the basic FOA and is referred to as θ-MAFOA. Mutation adaptation mechanism is adopted to enhance the balance of FOA in terms of the exploitation and exploration ability, while phase angle-based encoded strategy for fruit fly locations helps to achieve the high performance in the convergence process. Then, the proposed θ-MAFOA is used to find the optimal flight path for UAV in the complex three-dimensional (3D) terrain environments with various ground defense weapons. B-Spline curve that connects the start and target points is employed to represent a smooth path. Several performance criteria and constraints are taken into consideration to evaluate the cost of UAV paths. Numerical experiments are carried out on various test scenarios and the results show the proposed θ-MAFOA is superior to the basic and other two modified versions of FOA, and also more powerful than several existing state-of-the-art optimization algorithms.