A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for Cooperative Air Combat DWTA. (11th April 2017)

• Record Type: Journal Article
• Authors: Peng, Guang; Fang, Yangwang; Chen, Shaohua; Peng, Weishi; Yang, Dandan
• Other Names: Gong Maoguo Academic Editor.
• Is Part Of: Journal of optimization. Volume 2017(2017)
• Publication Date: 2017-04-11
• Subjects: Mathematical optimization -- Periodicals; Structural optimization -- Periodicals; Mathematical optimization; Structural optimization; Periodicals; Electronic journals; 519.6
• DOI: 10.1155/2017/8063767
• Languages: English
• ISSNs: 2356-752X

Abstract

Abstract : A hybrid multiobjective discrete particle swarm optimization (HMODPSO) algorithm is proposed to solve cooperative air combat dynamic weapon target assignment (DWTA). First, based on the threshold of damage probability and time window constraints, a new cooperative air combat DWTA multiobjective optimization model is presented, which employs the maximum of the target damage efficiency and minimum of ammunition consumption as two competitive objective functions. Second, in order to tackle the DWTA problem, a mixed MODPSO and neighborhood search algorithm is proposed. Furthermore, the repairing operator is introduced into the mixed algorithm, which not only can repair infeasible solutions but also can improve the quality of feasible solutions. Besides, the Cauchy mutation is adopted to keep the diversity of the Pareto optimal solutions. Finally, a typical two-stage DWTA scenario is performed by HMODPSO and compared with three other state-of-the-art algorithms. Simulation results verify the effectiveness of the new model and the superiority of the proposed algorithm.