Abstract
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.
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References
Van Laarhoven, P.J., Aarts, E.H.: Simulated annealing. Simulated Annealing: Theory and Applications, pp. 7–15. Springer, Berlin(1987)
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)
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)
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)
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)
Javidrad, F., Nazari, M.: A new hybrid particle swarm and simulated annealing stochastic optimization method. Appl. Soft Comput. 60, 634–654 (2017)
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)
Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8, 279–292 (1992)
Samma, H., Lim, C.P., Saleh, J.M.: A new reinforcement learning-based memetic particle swarm optimizer. Appl. Soft Comput. 43, 276–297 (2016)
Arora, J.: Introduction to Optimum Design. Elsevier, New York (2004)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings., IEEE International Conference on Neural Networks, vol. 1944, pp. 1942–1948 (1995)
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)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
MacKinnon, J.G.: Bootstrap hypothesis testing. Handb. Comput. Econ. 183, 213 (2009)
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Samma, H., Mohamad-Saleh, J., Suandi, S.A. (2019). Enhanced Simulated Annealing for Constrained Design Problems. In: Zawawi, M., Teoh, S., Abdullah, N., Mohd Sazali, M. (eds) 10th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-13-6447-1_4
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DOI: https://doi.org/10.1007/978-981-13-6447-1_4
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