Parametric optimization technique for continuous and combinational problems based on simulated annealing algorithm
DOI:
https://doi.org/10.22219/jemmme.v8i2.29556Keywords:
annealing, optimization, uphill, probabilistic, metaheuristicAbstract
Simulated annealing is an optimization method adapted from the annealing process. The optimization process using simulated annealing method is done by mapping the elements of physical coolant process onto the elements of optimization problem. This method uses local neighborhood search to find solutions, meaning it searches around it for answers itself and takes another solution based on everything around it. The simulated annealing method has been used successfully for the optimization process in the continuous case (Himmelblau’s function) and combinational case (Quadratic Assignment Problem or QAP). Based on the optimization results (global minima) for the Himmelblau's function, the points and are obtained with objective function . The optimal solution for the eight departmental arrangements is F, E, A, G for the bottom floor and H, D, C, B for the top floor, this arrangement produces an optimal total cost of 214. The simulated annealing method accepts an uphill move (worse move) by considering the probability, in this way we will not be trapped in the local minima position. These four search space variables and determine the performance of the simulated annealing method, we can adjust them according to the optimized case.Simulated annealing is an optimization method adapted from the annealing process. The optimization process using simulated annealing method is done by mapping the elements of physical coolant process onto the elements of optimization problem. This method uses local neighborhood search to find solutions, meaning it searches around it for answers itself and takes another solution based on everything around it. The simulated annealing method has been used successfully for the optimization process in the continuous case (Himmelblau’s function) and combinational case (Quadratic Assignment Problem or QAP). Based on the optimization results (global minima) for the Himmelblau's function, the points and are obtained with objective function . The optimal solution for the eight departmental arrangements is F, E, A, G for the bottom floor and H, D, C, B for the top floor, this arrangement produces an optimal total cost of 214. The simulated annealing method accepts an uphill move (worse move) by considering the probability, in this way we will not be trapped in the local minima position. These four search space variables and determine the performance of the simulated annealing method, we can adjust them according to the optimized case.
Downloads
References
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
Aydin, M. E., & Fogarty, T. C. (2004). A distributed evolutionary simulated annealing algorithm for combinatorial optimisation problems. Journal of heuristics, 10, 269-292.
Ahmed, A., Al-Maamari, M., Firouz, M., & Delen, D. (2023). An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations. Information Systems Frontiers, 1-21.
Agostini, F. P., Soares‐Pinto, D. D. O., Moret, M. A., Osthoff, C., & Pascutti, P. G. (2006). Generalized simulated annealing applied to protein folding studies. Journal of computational chemistry, 27(11), 1142-1155.
Xu, Q., Zhang, G., Zhao, C., & An, A. (2011, May). A robust adaptive hybrid genetic simulated annealing algorithm for the global optimization of multimodal functions. In 2011 Chinese Control and Decision Conference (CCDC) (pp. 7-12). IEEE.
Mu, D., Wang, C., Zhao, F., & Sutherland, J. W. (2016). Solving vehicle routing problem with simultaneous pickup and delivery using parallel simulated annealing algorithm. International Journal of Shipping and Transport Logistics, 8(1), 81-106.
Osamy, W., El-sawy, A. A., & Khedr, A. M. (2019). SATC: A simulated annealing based tree construction and scheduling algorithm for minimizing aggregation time in wireless sensor networks. Wireless Personal Communications, 108(2), 921-938.
Wu, Y., Tang, M., & Fraser, W. (2012, October). A simulated annealing algorithm for energy efficient virtual machine placement. In 2012 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 1245-1250). IEEE.
Zhang, Y., Han, X., Dong, Y., Xie, J., Xie, G., & Xu, X. (2021). A novel state transition simulated annealing algorithm for the multiple traveling salesmen problem. The Journal of Supercomputing, 77, 11827-11852.
Tanha, M., Hosseini Shirvani, M., & Rahmani, A. M. (2021). A hybrid meta-heuristic task scheduling algorithm based on genetic and thermodynamic simulated annealing algorithms in cloud computing environments. Neural Computing and Applications, 33, 16951-16984.
Downloads
Published
How to Cite
Issue
Section
License
![Creative Commons License](http://i.creativecommons.org/l/by-nc/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
![Creative Commons License](https://i.creativecommons.org/l/by-nc/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.