An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning.
Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.
School of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
Bai
0000-0002-8966-8992
B
Li
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
Li-gang
LG
Gong
School of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
Wen-lun
WL
Yang
D000465:Algorithms; D008962:Models, Theoretical