A Spider Monkey Optimization Based on Local Search-Based Heuristic Method for Unmanned Combat Aerial Vehicle (UCAV) Path Planning

The Unmanned Combat Aerial Vehicle (UCAV) path planning is a typically complicated global optimization problem. It seeks an optimal or near-optimal flight path in a complex battlefield environment, characterized by a minimal military risk factor and less constrained. The sensitivity and the importance of this military areal task, whose slightest mistake can cost considerable damage, involves the use of highly sophisticated driving methods. In fact, Swarm intelligence algorithms are considered as one of very effective alternative used to deal with latter, due to their capability and flexibility to address complex optimization problems. In this paper, the Spider Monkey Optimization (SMO) algorithm is combined with the so-called Hill Climbing Optimizer (HCO) to improve its exploration and its exploitation capabilities. Indeed, the hybridization mechanism is based on the use of this optimizer under its standard form, to improve firstly each new Spider Monkey (SM) solution (position) generated in the SMO Local Leader Phase, and secondly each new Spider Monkey (SM) solution (position) produced in the SMO Global Leader Phase. Experimental results demonstrate that our proposed method is more competitive than other state-of-the-art evolutionary algorithms for UCAV path planning problem considering the quality and the stability of the final paths.