American Institute of Mathematical Sciences

Advanced
  • Previous Article
    A metaheuristic method for vehicle routing problem based on improved ant colony optimization and Tabu search
  • JIMO Home
  • This Issue
  • Next Article
    An efficient convexification method for solving generalized geometric problems
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 and 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.

[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 1st 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 5th 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.

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.

[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 1st 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 5th 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.

[1]

Mostafa Abouei Ardakan, A. Kourank Beheshti, S. Hamid Mirmohammadi, Hamed Davari Ardakani. A hybrid meta-heuristic algorithm to minimize the number of tardy jobs in a dynamic two-machine flow shop problem. Numerical Algebra, Control and Optimization, 2017, 7 (4) : 465-480. doi: 10.3934/naco.2017029
[2]

Karsten Keller, Sergiy Maksymenko, Inga Stolz. Entropy determination based on the ordinal structure of a dynamical system. Discrete and Continuous Dynamical Systems - B, 2015, 20 (10) : 3507-3524. doi: 10.3934/dcdsb.2015.20.3507
[3]

Ruiqiang Guo, Lu Song. Optical chaotic secure algorithm based on space laser communication. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1355-1369. doi: 10.3934/dcdss.2019093
[4]

Huiyi Bao, Tao Du, Luyue Sun. Adaptive attitude determination of bionic polarization integrated navigation system based on reinforcement learning strategy. Mathematical Foundations of Computing, 2022  doi: 10.3934/mfc.2022014
[5]

Chia-Huang Wu, Kuo-Hsiung Wang, Jau-Chuan Ke, Jyh-Bin Ke. A heuristic algorithm for the optimization of M/M/$s$ queue with multiple working vacations. Journal of Industrial and Management Optimization, 2012, 8 (1) : 1-17. doi: 10.3934/jimo.2012.8.1
[6]

Mohamed Baouch, Juan Antonio López-Ramos, Blas Torrecillas, Reto Schnyder. An active attack on a distributed Group Key Exchange system. Advances in Mathematics of Communications, 2017, 11 (4) : 715-717. doi: 10.3934/amc.2017052
[7]

Yazheng Dang, Fanwen Meng, Jie Sun. Convergence analysis of a parallel projection algorithm for solving convex feasibility problems. Numerical Algebra, Control and Optimization, 2016, 6 (4) : 505-519. doi: 10.3934/naco.2016023
[8]

Rongliang Chen, Jizu Huang, Xiao-Chuan Cai. A parallel domain decomposition algorithm for large scale image denoising. Inverse Problems and Imaging, 2019, 13 (6) : 1259-1282. doi: 10.3934/ipi.2019055
[9]

Leyu Hu, Wenxing Zhang, Xingju Cai, Deren Han. A parallel operator splitting algorithm for solving constrained total-variation retinex. Inverse Problems and Imaging, 2020, 14 (6) : 1135-1156. doi: 10.3934/ipi.2020058
[10]

Kien Ming Ng, Trung Hieu Tran. A parallel water flow algorithm with local search for solving the quadratic assignment problem. Journal of Industrial and Management Optimization, 2019, 15 (1) : 235-259. doi: 10.3934/jimo.2018041
[11]

Liangliang Sun, Fangjun Luan, Yu Ying, Kun Mao. Rescheduling optimization of steelmaking-continuous casting process based on the Lagrangian heuristic algorithm. Journal of Industrial and Management Optimization, 2017, 13 (3) : 1431-1448. doi: 10.3934/jimo.2016081
[12]

Mao Chen, Xiangyang Tang, Zhizhong Zeng, Sanya Liu. An efficient heuristic algorithm for two-dimensional rectangular packing problem with central rectangle. Journal of Industrial and Management Optimization, 2020, 16 (1) : 495-510. doi: 10.3934/jimo.2018164
[13]

Palash Sarkar, Shashank Singh. A unified polynomial selection method for the (tower) number field sieve algorithm. Advances in Mathematics of Communications, 2019, 13 (3) : 435-455. doi: 10.3934/amc.2019028
[14]

Le Thi Hoai An, Tran Duc Quynh, Kondo Hloindo Adjallah. A difference of convex functions algorithm for optimal scheduling and real-time assignment of preventive maintenance jobs on parallel processors. Journal of Industrial and Management Optimization, 2014, 10 (1) : 243-258. doi: 10.3934/jimo.2014.10.243
[15]

Ran Ma, Jiping Tao. An improved 2.11-competitive algorithm for online scheduling on parallel machines to minimize total weighted completion time. Journal of Industrial and Management Optimization, 2018, 14 (2) : 497-510. doi: 10.3934/jimo.2017057
[16]

Min Li, Maoan Han. On the number of limit cycles of a quartic polynomial system. Discrete and Continuous Dynamical Systems - S, 2021, 14 (9) : 3167-3181. doi: 10.3934/dcdss.2020337
[17]

Yuanshi Wang. Asymmetric diffusion in a two-patch mutualism system characterizing exchange of resource for resource. Discrete and Continuous Dynamical Systems - B, 2021, 26 (2) : 963-985. doi: 10.3934/dcdsb.2020149
[18]

Aleksa Srdanov, Radiša Stefanović, Aleksandra Janković, Dragan Milovanović. "Reducing the number of dimensions of the possible solution space" as a method for finding the exact solution of a system with a large number of unknowns. Mathematical Foundations of Computing, 2019, 2 (2) : 83-93. doi: 10.3934/mfc.2019007
[19]

Louis Caccetta, Syarifah Z. Nordin. Mixed integer programming model for scheduling in unrelated parallel processor system with priority consideration. Numerical Algebra, Control and Optimization, 2014, 4 (2) : 115-132. doi: 10.3934/naco.2014.4.115
[20]

Junmin Yang, Maoan Han. On the number of limit cycles of a cubic Near-Hamiltonian system. Discrete and Continuous Dynamical Systems, 2009, 24 (3) : 827-840. doi: 10.3934/dcds.2009.24.827

2021 Impact Factor: 1.411

Tools

Metrics

  • PDF downloads (100)
  • HTML views (0)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2022 American Institute of Mathematical Sciences | Privacy Policy