Browse > Article
http://dx.doi.org/10.11627/jkise.2017.40.4.260

An Application of Quantum-inspired Genetic Algorithm for Weapon Target Assignment Problem  

Kim, Jung Hun (Department of Industrial and Management Engineering, Hannam University)
Kim, Kyeongtaek (Department of Industrial and Management Engineering, Hannam University)
Choi, Bong-Wan (Department of Industrial and Management Engineering, Hannam University)
Suh, Jae Joon (Department of Industrial and Management Engineering, Hanbat National University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.40, no.4, 2017 , pp. 260-267 More about this Journal
Abstract
Quantum-inspired Genetic Algorithm (QGA) is a probabilistic search optimization method combined quantum computation and genetic algorithm. In QGA, the chromosomes are encoded by qubits and are updated by quantum rotation gates, which can achieve a genetic search. Asset-based weapon target assignment (WTA) problem can be described as an optimization problem in which the defenders assign the weapons to hostile targets in order to maximize the value of a group of surviving assets threatened by the targets. It has already been proven that the WTA problem is NP-complete. In this study, we propose a QGA and a hybrid-QGA to solve an asset-based WTA problem. In the proposed QGA, a set of probabilistic superposition of qubits are coded and collapsed into a target number. Q-gate updating strategy is also used for search guidance. The hybrid-QGA is generated by incorporating both the random search capability of QGA and the evolution capability of genetic algorithm (GA). To observe the performance of each algorithm, we construct three synthetic WTA problems and check how each algorithm works on them. Simulation results show that all of the algorithm have good quality of solutions. Since the difference among mean resulting value is within 2%, we run the nonparametric pairwise Wilcoxon rank sum test for testing the equality of the means among the results. The Wilcoxon test reveals that GA has better quality than the others. In contrast, the simulation results indicate that hybrid-QGA and QGA is much faster than GA for the production of the same number of generations.
Keywords
Weapon Target Assignment; Genetic Algorithm; Quantum-inspired Genetic Algorithm;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Rafael, L.B., Quantum Genetic Algorithm for Computer Scientists Computers 2016. 5. 24, http://www.mdpi.com/journal/computer.
2 Roux, J.N. and van Vuuren, J.H., Threat Evaluation and Weapon Assignment Decision Support : A Review of the State-of-the-Art, ORiON, Journal of ORSSA, 2007, Vol. 23, No. 2, pp. 151-187.
3 Wacholder, E., A Neural Network-based Optimization Algorithm for the Static Weapon-Target Assignment Problem, ORSA Journal of Computing, 1989, Vol. 1, No. 4, pp. 232-246.   DOI
4 Yim, D.-S. and Choi, B.W., Performance Comparison of Heuristics for Weapon-Target Assignment Problem with Transitivity Rules in Weapon's Kill Probability, MORSK, 2010, Vol. 36, No. 3, pp. 29-42
5 Zeng, X., Zhu, Y., Nan, L., Hu, K., Niu, B., and He, X., Solving Weapon Target Assignment Problem using Discrete Particle Swarm Optimization, Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, 2006, pp. 3562-3565.
6 Bisht, S., Hybrid Genetic-Simulated Annealing Algorithm for Optimal Weapon Allocation in Multilayer Defense Scenario, Defense Science Journal, 2004, Vol. 54, No. 3, pp. 395-405.   DOI
7 Hong, S.S. and Han, M.M., A Study of Population Initialization Method to improve a Genetic Algorithm on the Weapon Target Allocation Problem, Journal of Korean Institute of Intelligent Systems, Vol. 22, No. 5, 2012, pp. 540-548.   DOI
8 Fu, T., Liu, Y., and Chen, J., Improved Genetic & Ant Colony Optimization for Regional Air Defense WTA Problem, Proceedings of the 1st International Conference on Innovative Computers, Information, Control, Dalian, China, 2006, pp. 226-229.
9 Han, K.H. and Kim, J.H., On Setting the Parameters Of Quantum-inspired Evolutionary Algorithm for Practical Applications, http://koasas.kaist.ac.kr/handle/10203/11267.
10 Han, K.H. and Kim, J.H., Quantum-Inspired Evolutionary Algorithm for Class of Combinatorial Optimization, IEEE Transaction on Evolutionary Computation, Vol. 6, No. 6, 2002, pp. 580-593.   DOI
11 Hosein P.A. and Athans, M., Some Analytical Results for the Dynamic Weapon-Target Allocation problem, MIT Technology Report LIDSP-1944, 1990, Cambridge, USA.
12 Lee, Z.J., Lee, C.Y., and Su, S.F., An Immunity based Ant Colony Optimization Algorithm for Solving Weapon- Target Assignment Problem, Applied Soft Computing, 2002, Vol. 2, No. 1, pp. 39-47.   DOI
13 Hosein, P.A. and Athans, M., Preferential Defense Strategies, Part I : The Static Case, MIT Technology Report LIPS-P-2002, 1990, Cambridge, USA.
14 Jang, J.G., Kim, K.T., Choi, B.W., and Suh, J.J., A Linear Approximation Model for an Asset-based Weapon Target Assignment Problem, Journal of Korean Institute of Intelligent Systems, Vol. 38, No. 3, 2015, pp. 108-116.
15 Johansson, F., Evaluating the Performance of TEWA System, Ph.D. Dissertation, 2010, Orebro University, Sweden.
16 Karasakal, O., Air Defense Missile-Target Allocation for a Naval Task Group, Computers & Operations Research, 2008, Vol. 35, pp. 1759-1770.   DOI
17 Lee, D.R. and Yang, J.W., An Efficient Genetic Algorithm for the Allocation and Engagement Scheduling of Interceptor Missile, Journal of Society of Korea Industrial and Systems Engineering, Vol. 39, No. 2, 2016, pp. 88-102.   DOI
18 Lloyd, S.P. and Witsenhausen, H.S., Weapon Allocation is NP-Complete, Proceedings of the IEEE Summer Computer Simulation Conference, 1986, pp. 1054-1058.
19 Lu, H., Zhang, H., Zhang, X., and Han, R., An Improved Genetic Algorithm for Target Assignment Optimization of Naval Fleet Air Defense, Proceedings of the 6th World Congress on Intelligent Control & Automation, Dalian, China, 2006, pp. 3401-3405.
20 Metler, W.A. and Preston, F.L., Solutions to a Probabilistic Resource Allocation Problem, Proceedings of the 28th IEEE Conference on Decision and Control, Tempa, Florida, USA, 1989, Vol. 2, pp. 1606-1611.
21 Murphey, R.A., Target-based Weapon Target Assignment Problems, Nonlinear assignment Problems : Algorithms and Applications, Kluwer Academic Publishers, 2000, pp. 39-53.