A Comparative Study of Swarm Intelligence Algorithms for UCAV Path-Planning Problems

Path-planning for uninhabited combat air vehicles (UCAV) is a typically complicated global optimization problem. It seeks superior flight path in complex battlefield environment, taking into various constraints. Many swarm intelligence (SI) algorithms have recently gained remarkable attention due to their capability address problems. However, different SI present performances UCAV path-planning since each algorithm has its own strengths and weaknesses. Therefore, this study provides an overview of research. In the experiment, twelve that published major journals conference proceedings are surveyed then applied path-planning. Moreover, demonstrate performance further, we design scales problem cases those comparative algorithms. The experimental results show can find safe avoid threats efficiently based on most particular, Spider Monkey Optimization more effective robust than other handling analysis from perspectives contributes highlight trends open issues field UCAVs.