Distributed MMAS for weapon target assignment based on Spark framework

Article type: Research Article

Authors: Cao, Ming^{; *} | Fang, Weiguo

Affiliations: School of Economics and Management, Beihang University, Beijing, China

Correspondence: [*] Corresponding author. Ming Cao, School of Economics and Management, Beihang University, Beijing 100191, China. E-mail: [email protected].

Abstract: Weapon target allocation (WTA) is a classic NP-complete problem in the field of military operations research. In this paper, we addressed the multi-constraint WTA problems in multilayer defense scenario. To solve large-scale WTA problems effectively, a distributed MAX-MIN Ant System (MMAS) algorithm based on distributed computing framework Spark was developed and improved. An experiment environment comprising virtual machines was built for implementing the distributed MMAS. First, a small-scale WTA example, whose theoretical optimal solution can be obtained by existing optimization software, was taken as a benchmark problem to assess the performance of distributed MMAS. The result shows that it can find high-quality and robust approximate solutions. Then a large-scale WTA problem was constructed and used to further evaluate the performance of distributed MMAS in the experiment environment. The result shows that the distributed MMAS can also achieve high-quality approximate solutions with high robustness and computational efficiency even for large scale WTA problems. Our study demonstrates it is a promising approach for solving large-scale iteration-dependent optimization problems like WTA by means of incorporating heuristic optimization algorithms such as Ant Colony Optimization into distributed computing framework.

Keywords: Weapon target allocation (WTA), distributed computing, Spark, heuristic algorithm, MAX-MIN ant system (MMAS).

DOI: 10.3233/JIFS-18482

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 3, pp. 3685-3696, 2018