Enhanced Simulated Annealing for Constrained Design Problems

  • Hussein SammaEmail author
  • Junita Mohamad-Saleh
  • Shahrel Azmin Suandi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 547)


Real-world design problems such as welded beam design, pressure vessel design, and three-bar truss design were recognized as challenging tasks due to the associated constraints. This work aims to develop an Enhanced Simulated Annealing (ESA) optimizer that embeds the Q-learning algorithm in order to control its execution at run time. Specifically, the Q-learning algorithm is used to guide SA toward the best performing value of the annealing factor at run-time. To assess the performance of ESA, a total of four popular constrained engineering design problems were conducted. The outcomes reveal the ability of ESA to significantly overcome the standard SA as well as other optimization algorithms such as GWO, PSO, and CLPSO.


  1. 1.
    Van Laarhoven, P.J., Aarts, E.H.: Simulated annealing. Simulated Annealing: Theory and Applications, pp. 7–15. Springer, Berlin(1987)Google Scholar
  2. 2.
    Ezugwu, A.E.-S., Adewumi, A.O., Frîncu, M.E.: Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem. Expert Syst. Appl. 77, 189–210 (2017)CrossRefGoogle Scholar
  3. 3.
    Torkaman, S., Ghomi, S.F., Karimi, B.: Hybrid simulated annealing and genetic approach for solving a multi-stage production planning with sequence-dependent setups in a closed-loop supply chain. Applied Soft Computing (2017)Google Scholar
  4. 4.
    AL-Qutami, T.A., Ibrahim, R., Ismail, I., Ishak, M.A.: Virtual multiphase flow metering using diverse neural network ensemble and adaptive simulated annealing. Expert Syst. Appl. 93, 72–85 (2018)Google Scholar
  5. 5.
    Wei, L., Zhang, Z., Zhang, D., Leung, S.C.: A simulated annealing algorithm for the capacitated vehicle routing problem with two-dimensional loading constraints. Eur. J. Oper. Res. 265, 843–859 (2018)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Javidrad, F., Nazari, M.: A new hybrid particle swarm and simulated annealing stochastic optimization method. Appl. Soft Comput. 60, 634–654 (2017)CrossRefGoogle Scholar
  7. 7.
    Liu, Z., Liu, Z., Zhu, Z., Shen, Y., Dong, J.: Simulated annealing for a multi-level nurse rostering problem in hemodialysis service. Appl. Soft Comput. 64, 148–160 (2018)CrossRefGoogle Scholar
  8. 8.
    Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8, 279–292 (1992)Google Scholar
  9. 9.
    Samma, H., Lim, C.P., Saleh, J.M.: A new reinforcement learning-based memetic particle swarm optimizer. Appl. Soft Comput. 43, 276–297 (2016)CrossRefGoogle Scholar
  10. 10.
    Arora, J.: Introduction to Optimum Design. Elsevier, New York (2004)Google Scholar
  11. 11.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings., IEEE International Conference on Neural Networks, vol. 1944, pp. 1942–1948 (1995)Google Scholar
  12. 12.
    Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10, 281–295 (2006)CrossRefGoogle Scholar
  13. 13.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  14. 14.
    MacKinnon, J.G.: Bootstrap hypothesis testing. Handb. Comput. Econ. 183, 213 (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Hussein Samma
    • 1
    Email author
  • Junita Mohamad-Saleh
    • 1
  • Shahrel Azmin Suandi
    • 1
  1. 1.School of Electrical and Electronic EngineeringUniversiti Sains MalaysiaNibong TebalMalaysia

Personalised recommendations