Comparative Performance Analysis of Particle Swarm Optimization and Artificial Bee Colony Algorithm for Optimization of Missile Gliding Trajectory

Abstract

Swarm intelligence algorithms were widely employed for trajectory optimization problem. The current study presents a comparative performance analysis of two well known swarm intelligence algorithms such as particle swarm optimization (PSO) and artificial bee colony (ABC) algorithm for optimization of missile gliding trajectory. The gliding range was maximized through trajectory optimization of missile by descretizing the angle of attack (AOA) as control parameter and solving control problem. Performance characteristics of PSO and ABC were evaluated based on computational efficiency, accuracy of solution and convergence ability. The obtained results reveal the superior performance of PSO with regard to accuracy of solution, computational efficacy and convergence ability in comparison to ABC.