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April  2012, 8(2): 457-468. doi: 10.3934/jimo.2012.8.457

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, China
2. 

Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin, 150001, China

Received  March 2011 Revised  November 2011 Published  April 2012

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: adjacent exchange, heuristic algorithm., Laser antimissile system, relative ordinal number, parallel navigation.
Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C3.
Citation: Xiangyu Gao, Yong Sun. A new heuristic algorithm for laser antimissile strategy optimization. Journal of Industrial & Management Optimization, 2012, 8 (2) : 457-468. doi: 10.3934/jimo.2012.8.457
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.  Google Scholar

[2]

C. G. Cao, "Linear Algebra,'' Inner Mongolia Science and Technology Press, 1999. Google Scholar

[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.  Google Scholar

[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.  Google Scholar

[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.  Google Scholar

[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.  Google Scholar

[7]

J. J. Grefenstette, R. Gopal, B. J. Rosmaita and D. V. Gucht, Genetic algorithms for the traveling salesman problem, in "Proceedings of the 1st International Conference on Genetic Algorithms," Erlbaum, Hillsdale, NJ, (1985), 160-168. Google Scholar

[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.  Google Scholar

[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.  Google Scholar

[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. Google Scholar

[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. Google Scholar

[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.  Google Scholar

[13]

H. S. Lope and L. S. Coelho, Particle swarm optimization with fast local search for the blind traveling salesman problem, "Proceedings of the 5th International Conference on Hybrid Intelligent Systems," New York, (2005), 245-250. Google Scholar

[14]

T. A. J. Nicholson, "Optimization in Industry,'' Aldine Transaction, 2007. Google Scholar

[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.  Google Scholar

[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.  Google Scholar

[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.  Google Scholar

[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. Google Scholar

[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.  Google Scholar

[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.  Google Scholar

show all references

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.  Google Scholar

[2]

C. G. Cao, "Linear Algebra,'' Inner Mongolia Science and Technology Press, 1999. Google Scholar

[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.  Google Scholar

[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.  Google Scholar

[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.  Google Scholar

[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.  Google Scholar

[7]

J. J. Grefenstette, R. Gopal, B. J. Rosmaita and D. V. Gucht, Genetic algorithms for the traveling salesman problem, in "Proceedings of the 1st International Conference on Genetic Algorithms," Erlbaum, Hillsdale, NJ, (1985), 160-168. Google Scholar

[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.  Google Scholar

[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.  Google Scholar

[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. Google Scholar

[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. Google Scholar

[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.  Google Scholar

[13]

H. S. Lope and L. S. Coelho, Particle swarm optimization with fast local search for the blind traveling salesman problem, "Proceedings of the 5th International Conference on Hybrid Intelligent Systems," New York, (2005), 245-250. Google Scholar

[14]

T. A. J. Nicholson, "Optimization in Industry,'' Aldine Transaction, 2007. Google Scholar

[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.  Google Scholar

[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.  Google Scholar

[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.  Google Scholar

[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. Google Scholar

[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.  Google Scholar

[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.  Google Scholar

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