A strategic conflict avoidance approach based on cooperative coevolutionary with the dynamic grouping strategy. Issue 9 (3rd July 2016)

• Record Type: Journal Article
• Authors: Guan, Xiangmin; Zhang, Xuejun; Wei, Jian; Hwang, Inseok; Zhu, Yanbo; Cai, Kaiquan
• Is Part Of: International journal of systems science. Volume 47:Issue 9(2016)
• Page Start: 1995
• Page End: 2008
• Publication Date: 2016-07-03
• Subjects: conflict avoidance -- large-scale flight planning -- dynamic grouping strategy -- cooperative coevolution; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2014.966282
• Languages: English
• ISSNs: 0020-7721

Abstract

Abstract : Conflict avoidance plays a crucial role in guaranteeing the safety and efficiency of the air traffic management system. Recently, the strategic conflict avoidance (SCA) problem has attracted more and more attention. Taking into consideration the large-scale flight planning in a global view, SCA can be formulated as a large-scale combinatorial optimisation problem with complex constraints and tight couplings between variables, which is difficult to solve. In this paper, an SCA approach based on the cooperative coevolution algorithm combined with a new decomposition strategy is proposed to prevent the premature convergence and improve the search capability. The flights are divided into several groups using the new grouping strategy, referred to as the dynamic grouping strategy, which takes full advantage of the prior knowledge of the problem to better deal with the tight couplings among flights through maximising the chance of putting flights with conflicts in the same group, compared with existing grouping strategies. Then, a tuned genetic algorithm (GA) is applied to different groups simultaneously to resolve conflicts. Finally, the high-quality solutions are obtained through cooperation between different groups based on cooperative coevolution. Simulation results using real flight data from the China air route network and daily flight plans demonstrate that the proposed algorithm can reduce the number of conflicts and the average delay effectively, outperforming existing approaches including GAs, the memetic algorithm, and the cooperative coevolution algorithms with different well-known grouping strategies.