A new search scheme using multi‐bee‐colony elite learning method for unmanned aerial vehicles in unknown environments. (17th June 2022)

• Record Type: Journal Article
• Authors: Yue, Wei; Li, Chaofan; Liu, Zhongchang; Li, Lili; Wang, Liyuan
• Is Part Of: Optimal control applications and methods. Volume 43:Number 6(2022)
• Page Start: 1645
• Page End: 1664
• Publication Date: 2022-06-17
• Subjects: multi‐bee colony algorithm -- collaborative search -- elite learning -- different flight altitudes; Control theory -- Periodicals; Mathematical optimization -- Periodicals; 629.8312
• DOI: 10.1002/oca.2918
• Languages: English
• ISSNs: 0143-2087

Abstract

Abstract: In this research, a cooperative search for multiple dynamic targets in an unknown marine environment by multiple unmanned aerial vehicles is studied based on a novel multi‐bee‐colony (MBC) elite learning algorithm. First, a specialized searching model is established which includes the UAV dynamics, the sensor model, the target probability and environmental certainty at different flight altitudes. Then, a new search strategy, which consists of rough search and accurate search, is proposed by maximizing the multiobjective utility function with the dynamic changing of the flight altitude. In order to solve the optimization problem, an improved MBC algorithm based on elite learning is designed, which can improve the adaptability and computation speed of the standard artificial bee colony (ABC) algorithm under different search missions. Finally, extensive simulations are conducted to show the effectiveness and superiority of the proposed search strategy.