Simplified swarm optimization with initialization scheme for dynamic weapon–target​ assignment problem

• Published in: Applied soft computing Vol. 82; p. 105542
• Main Authors: Lai, Chyh-Ming, Wu, Tsung-Hua
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2019
• Subjects: Dynamic weapon–target assignment problem; Feasible solution; Simplified swarm optimization; Dynamic weapon–target assignment problem; Feasible solution; Simplified swarm optimization
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.105542

The dynamic weapon–target assignment (DWTA) problem is a critical issue in the field of military operations research. The problem is highly constrained; thus, the use of an evolutionary method to solve the DWTA problem often encounters a population of infeasible solutions resulting in prohibitive computational burden. Aiming at accelerating the solution process and improving the solution quality, this work proposes an improved simplified swarm optimization called SSODT with two novel schemes: the deterministic initialization scheme, and the target exchange scheme. The deterministic initialization scheme is used in population initialization and utilizes problem-specific knowledge of DWTA to speed up the convergence of SSODT by generating a promising feasible solution which has a greater potential for evolving globally. The target exchange scheme is a local search updating feasible solutions in a manner that exchanges their variables without violating the engagement feasibility to enhance the exploitation capability of SSODT. The proposed method is empirically verified on thirty-six artificial problems and compared with widely popular evolutionary methods. The results demonstrate that the proposed SSODT is better than its competitors in terms of both solution quality and efficiency. •The first work to apply SSO to the weapon–target assignment problem.•Proposed a novel initialization scheme to generate a promising feasible solution.•Proposed a local search to improve solutions under engagement feasibility constraints.•Statistical results indicate that the proposed method is better than its competitors.