Skip to main content
Log in

Time-optimal memetic whale optimization algorithm for hypersonic vehicle reentry trajectory optimization with no-fly zones

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

A novel time-optimal memetic whale optimization algorithm (WOA) integrating the Gauss pseudo-spectral methods (GPM), is proposed in this paper for the hypersonic vehicle entry trajectory optimization problem with no-fly zones. The WOA is featured with the strong global search ability and non-sensitive to the initial values, but also shows poor searching convergence speed around the global optimum. Conversely, GPM may be sensitive to the initial solution and easily trapped in a local optimum, but it also possesses more rapid convergence speed around the optimum and higher searching accuracy. Thus, a memetic optimization algorithm which contains a two-stage approach mechanism is proposed for searching the global optimum. The first searching stage, which is driven by an improved WOA (IWOA), works as an initializer of the entire searching due to its strong global search ability and non-sensitive to the initial values. The local optimum reservation and adaptive amplitude factor updating strategy are established to improve the convergent speed and the global search ability of the WOA. Once the changing of fitness value satisfies the predefined criterion, the next searching stage driven by GPM will take the place of the IWOA to expedite the search process around optimum and to obtain a precise global optimal solution. By this hybrid way, the proposed optimization algorithm may find an optimum more quickly and accurately. Simulation results show the proposed algorithm possesses faster convergence speed, higher accuracy, and stronger robustness for the hypersonic vehicle entry trajectory optimization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Lin CM, Tai CF, Chung CC (2014) Intelligent control system design for UAV using a recurrent wavelet neural network. Neural Comput Appl 24(2):487–496

    Article  Google Scholar 

  2. Agarwal A, Lim MH, Er MJ, Nguyen TN (2007) Rectilinear workspace partitioning for parallel coverage using multiple UAVs. Adv Robot 21(1):105–120

    Article  Google Scholar 

  3. Kan EM, Lim MH, Ong YS, Tan AH, Yeo SP (2013) Extreme learning machine terrain-based navigation for unmanned aerial vehicles. Neural Comput Appl 22(3–4):469–477

    Article  Google Scholar 

  4. Zhou M, Zhou J, Guo J (2015) Terminal area guidance for reusable launch vehicles. Proc Inst Mech Eng Part G J Aerosp Eng 230(2):333–349

    Article  Google Scholar 

  5. Zhao J, Zhou R (2013) Reentry trajectory optimization for hypersonic vehicle satisfying complex constraints. Chin J Aeronaut 26(6):1544–1553

    Article  Google Scholar 

  6. Jiang Z, Rui Z (2015) Particle swarm optimization applied to hypersonic reentry trajectories. Chin J Aeronaut 28(3):822–831

    Article  MathSciNet  Google Scholar 

  7. Su Z, Wang H (2015) A novel robust hybrid gravitational search algorithm for reusable launch vehicle approach and landing trajectory optimization. Neurocomputing 162:116–127

    Article  Google Scholar 

  8. Morani G, Cuciniello G, Corraro F, Vito VD (2011) On-line guidance with trajectory constraints for terminal area energy management of re-entry vehicles. Proc Inst Mech Eng Part G J Aerosp Eng 225(6):631–643

    Article  Google Scholar 

  9. Su Z, Wang H, Yao P (2016) A hybrid backtracking search optimization algorithm for nonlinear optimal control problems with complex dynamic constraints. Neurocomputing 186:182–194

    Article  Google Scholar 

  10. Mease KD, Chen DT, Schönenberger H, Teufel P, Mease KD, Chen DT et al (2002) Reduced-order entry trajectory planning for acceleration guidance. J Guid Control Dyn 25(2):257–266

    Article  Google Scholar 

  11. Zhang H, Cao X, Ho J, Chow T (2017) Object-level video advertising: an optimization framework. IEEE Trans Ind Inf 13(2):520–531

    Article  Google Scholar 

  12. Zhang H, Llorca J, Davis C, Milner S (2012) Nature-inspired self-organization, control, and optimization in heterogeneous wireless networks. IEEE Trand Mob Comput 11(7):1207–1222

    Article  Google Scholar 

  13. Xie Y, Liu L, Tang G, Zheng W (2013) Highly constrained entry trajectory generation. Acta Astronaut 88(3):44–60

    Article  Google Scholar 

  14. Betts JT (2012) Survey of numerical methods for trajectory optimization. J Guid Control Dyn 21(2):193–207

    Article  Google Scholar 

  15. Peng H, Shan J, Meng X (2013) Re-entry trajectory optimization using an hp-adaptive Radau pseudospectral method. Proc Inst Mech Eng Part G J Aerosp Eng 227(10):1623–1636

    Article  Google Scholar 

  16. Garg D, Hager WW, Rao AV (2011) Pseudospectral methods for solving infinite-horizon optimal control problems. Automatica 47(4):829–837

    Article  MathSciNet  Google Scholar 

  17. Huntington GT, Rao AV (2015) Comparison of global and local collocation methods for optimal control. J Guid Control Dyn 31(2):432–436

    Article  Google Scholar 

  18. Zhao J, Zhou R, Jin X (2014) Gauss pseudospectral method applied to multi-objective spacecraft trajectory optimization. J Comput Theor Nanostruct 11(10):2242–2246

    Article  Google Scholar 

  19. Bayón L, Grau JM, Ruiz MM, Suárez PM (2010) Initial guess of the solution of dynamic optimization of chemical processes. J Math Chem 48(1):28–37

    Article  MathSciNet  Google Scholar 

  20. Joseph J, Auwatanamongkol S (2016) A crowding multi-objective genetic algorithm for image parsing. Neural Comput Appl 27(8):2217–2227

    Article  Google Scholar 

  21. Wang G, Chu HCE, Zhang Y, Chen H, Hu W, Li Y et al (2015) Multiple parameter control for ant colony optimization applied to feature selection problem. Neural Comput Appl 26(7):1693–1708

    Article  Google Scholar 

  22. Altun AA, Şahman MA (2013) Cost optimization of mixed feeds with the particle swarm optimization method. Neural Comput Appl 22(2):383–390

    Article  Google Scholar 

  23. Das PK, Behera HS, Panigrahi BK (2016) A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm Evol Comput 28:14–28

    Article  Google Scholar 

  24. Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144

    MathSciNet  MATH  Google Scholar 

  25. Modares H, Sistani MBN (2011) Solving nonlinear optimal control problems using a hybrid IPSO–SQP algorithm. Eng Appl Artif Intell 24(3):476–484

    Article  Google Scholar 

  26. Zhuang Y, Huang H (2014) Time-optimal trajectory planning for underactuated spacecraft using a hybrid particle swarm optimization algorithm. Acta Astronaut 94(2):690–698

    Article  Google Scholar 

  27. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  28. Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312

    Article  Google Scholar 

  29. Aziz ME, Ewees AA, Hassanien AE (2017) Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Syst Appl 83:242–256

    Article  Google Scholar 

  30. Aljarah I, Faris H, Mirjalili S (2016) Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput. https://doi.org/10.1007/s00500-016-2442-1

    Article  Google Scholar 

  31. Nazari-Heris M, Mehdinejad M, Mohammadi-Ivatloo B et al (2017) Combined heat and power economic dispatch problem solution by implementation of whale optimization method. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3074-9

    Article  Google Scholar 

  32. Oliva D, Aziz MAE, Hassanien AE (2017) Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Appl Energy 200:141–154

    Article  Google Scholar 

  33. Yu Y, Wang H, Li N, Su Z, Wu J (2017) Automatic carrier landing system based on active disturbance rejection control with a novel parameters optimizer. Aerosp Sci Technol 69:149–160

    Article  Google Scholar 

  34. Subbarao K, Shippey BM (2012) Hybrid genetic algorithm collocation method for trajectory optimization. J Guid Control Dyn 32(4):1396–1403

    Article  Google Scholar 

  35. Betts JT, Kolmanovsky I (2001) Practical methods for optimal control using nonlinear programming. SIAM Press, Philadelphia

    Google Scholar 

  36. Mathur M, Karale SB, Priye S (2000) Ant colony approach to continuous function optimization. Ind Eng Chem Res 39(10):3814–3822

    Article  Google Scholar 

  37. Man KF, Tang KS, Kwong S (1996) Genetic algorithms: concepts and applications. IEEE Trans Ind Electron 43(5):519–534

    Article  Google Scholar 

  38. Joines JA, Houck CR (1994) On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA’s. In: Proceedings of the first IEEE conference on evolutionary computation, vol 2. IEEE, Washington, pp 579–584

  39. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  40. Rao AV, Benson DA, Darby C, Patterson MA, Francolin C, Sanders I et al (2011) GPOPS, a matlab software for solving multiple-phase optimal control problems using the gauss pseudospectral method. ACM Trans Math Softw 37:22–39

    MATH  Google Scholar 

  41. Gill PE, Murray W, Saunders MA (2005) Snopt: an SQP algorithm for large-scale constrained optimization. SIAM Rev 47(1):99–131

    Article  MathSciNet  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China under Grants 61673042 and 61175084.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Honglun Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Wang, H., Li, N. et al. Time-optimal memetic whale optimization algorithm for hypersonic vehicle reentry trajectory optimization with no-fly zones. Neural Comput & Applic 32, 2735–2749 (2020). https://doi.org/10.1007/s00521-018-3764-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3764-y

Keywords

Navigation