A new heuristic algorithm for laser antimissilestrategy optimization

Xiangyu Gao; Yong Sun

doi:10.3934/jimo.2012.8.457

Article Contents

2012, Volume 8, Issue 2: 457-468. Doi: 10.3934/jimo.2012.8.457

This issue Previous Article An efficient convexification method for solving generalized geometric problems Next Article A metaheuristic method for vehicle routing problem based on improved ant colony optimization and Tabu search

A new heuristic algorithm for laser antimissile strategy optimization

Xiangyu Gao^1, and
Yong Sun^2,

1.
School of Mathematical Science, Heilongjiang University, Harbin, 150080
2.
Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin, 150001

Received: February 28, 2011

Revised: October 31, 2011

Published: March 2012

Abstract Related Papers Cited by

Abstract

This paper presents a new heuristic algorithm for solving a class of dynamic laser antimissile problems. The main virtue of this algorithm is that it can find a satisfactory local optimal solution within allowable short time duration which is the rigid requirement in this application. Two examples are considered and solved to illustrate the effectiveness of algorithm proposed.

Keywords:

Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

Citation:

Related Papers

Cited by

References

[1]	B. D. Bacher, V. Furnon, P. Shaw, P. Kilby and P. Prosser, Solving vehicle routing problems using constraint programming and metaheuristics, Journal of Heuristics, 6 (2000), 501-523.doi: 10.1023/A:1009621410177.
[2]	C. G. Cao, "Linear Algebra,'' Inner Mongolia Science and Technology Press, 1999.
[3]	V. Černý, Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm, Journal of Optimization Theory and Applications, 45 (1985), 41-51.doi: 10.1007/BF00940812.
[4]	C. A. C. Coello and N. C. Cortés, Hybridizing a genetic algorithm with an artificial immune system for global optimization, Engineering Optimization, 36 (2004), 607-634.doi: 10.1080/03052150410001704845.
[5]	M. Dorigo and L. M. Gambardella, Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Transactions on Evolutionary Computation, 1 (1997), 53-66.doi: 10.1109/4235.585892.
[6]	Z. Ezziane, Applications of artificial intelligence in bioinformatics: A review, Expert Systems with Applications, 30 (2006), 2-10.doi: 10.1016/j.eswa.2005.09.042.
[7]	J. J. Grefenstette, R. Gopal, B. J. Rosmaita and D. V. Gucht, Genetic algorithms for the traveling salesman problem, in "Proceedings of the 1^st International Conference on Genetic Algorithms," Erlbaum, Hillsdale, NJ, (1985), 160-168.
[8]	M. Held and R. M. Karp, The traveling-salesman problem and minimum spanning trees, Operations Research, 18 (1970), 1138-1162.doi: 10.1287/opre.18.6.1138.
[9]	B. Jarboui, S. Ibrahim, P. Siarry and A. Rebai, A combinatorial particle swarm optimization for solving permutation flowshop problems, Computers and Industrial Engineering, 54 (2008), 526-538.doi: 10.1016/j.cie.2007.09.006.
[10]	B. A. Julstrom, Very greedy crossover in a genetic algorithm for the traveling salesman problem, in "Proceedings of the ACM Symposium on Applied Computing," ACM, New York, (1995), 324-328.
[11]	I. H. Kuo, S. J. Horng, T. W. Kao, T. L. Lin and P. Fan, An efficient flow-shop scheduling algorithm based on a hybrid particle swarm optimization model, Lecture Notes in Artificial Intelligence, 4570 (2007), 303-312.
[12]	S. Lin and B. W. Kernighan, An effective heuristic algorithm for the traveling-salesman problem, Operations Research, 21 (1973), 498-516.doi: 10.1287/opre.21.2.498.
[13]	H. S. Lope and L. S. Coelho, Particle swarm optimization with fast local search for the blind traveling salesman problem, "Proceedings of the 5^th International Conference on Hybrid Intelligent Systems," New York, (2005), 245-250.
[14]	T. A. J. Nicholson, "Optimization in Industry,'' Aldine Transaction, 2007.
[15]	M. Padberg and G. Rinaldi, A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems, SIAM Review, 33 (1991), 60-100.doi: 10.1137/1033004.
[16]	X. H. Shi, Y. C. Liang, H. P. Lee, C. Lu and Q. X. Wang, Particle swarm optimization-based algorithms for TSP and generalized TSP, Information Processing Letters, 103 (2007), 169-176.doi: 10.1016/j.ipl.2007.03.010.
[17]	M. F. Tasgetiren, Y. C. Liang, M. Sevkli and G. Gencyilmaz, A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem, European Journal of Operational Research, 177 (2007), 1930-1947.doi: 10.1016/j.ejor.2005.12.024.
[18]	C. H. Yang and K. E. Nygard, The effects of initial population in genetic search for time constrained traveling salesman problems, in "Proceedings of the ACM Conference on Computer Science," ACM, New York, (1993), 378-383.
[19]	A. R. Yildiz, A novel hybrid immune algorithm for global optimization in design and manufacturing, Robotics and Computer-Integrated Manufacturing, 25 (2009), 261-270.doi: 10.1016/j.rcim.2007.08.002.
[20]	A. R. Yildiz, A novel particle swarm optimization approach for product design and manufacturing, International Journal of Advanced Manufacturing Technology, 40 (2009), 617-628.doi: 10.1007/s00170-008-1453-1.