Enhanced simulated annealing for constrained design problems

Hussein Samma; Junita Mohamad-Saleh; Shahrel Azmin Suandi

doi:10.1007/978-981-13-6447-1_4

Enhanced simulated annealing for constrained design problems

Hussein Samma^*, Junita Mohamad-Saleh, Shahrel Azmin Suandi

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	10th International Conference on Robotics, Vision, Signal Processing and Power Applications - Enabling Research and Innovation Towards Sustainability
Editors	Mohamad Adzhar Md Zawawi, Soo Siang Teoh, Noramalina Binti Abdullah, Mohd Ilyas Sobirin Mohd Sazali
Publisher	Springer Verlag
Pages	27-33
Number of pages	7
ISBN (Print)	9789811364464
DOIs	https://doi.org/10.1007/978-981-13-6447-1_4
State	Published - 2019
Externally published	Yes
Event	10th International Conference on Robotic, Vision, Signal Processing and Power Applications, ROVISP 2018 - Penang, Malaysia Duration: 14 Aug 2018 → 15 Aug 2018

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	547
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	10th International Conference on Robotic, Vision, Signal Processing and Power Applications, ROVISP 2018
Country/Territory	Malaysia
City	Penang
Period	14/08/18 → 15/08/18

Bibliographical note

Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2019.

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-13-6447-1_4

Cite this

Samma, H., Mohamad-Saleh, J., & Suandi, S. A. (2019). Enhanced simulated annealing for constrained design problems. In M. A. Md Zawawi, S. S. Teoh, N. B. Abdullah, & M. I. S. Mohd Sazali (Eds.), 10th International Conference on Robotics, Vision, Signal Processing and Power Applications - Enabling Research and Innovation Towards Sustainability (pp. 27-33). (Lecture Notes in Electrical Engineering; Vol. 547). Springer Verlag. https://doi.org/10.1007/978-981-13-6447-1_4

Samma, Hussein ; Mohamad-Saleh, Junita ; Suandi, Shahrel Azmin. / Enhanced simulated annealing for constrained design problems. 10th International Conference on Robotics, Vision, Signal Processing and Power Applications - Enabling Research and Innovation Towards Sustainability. editor / Mohamad Adzhar Md Zawawi ; Soo Siang Teoh ; Noramalina Binti Abdullah ; Mohd Ilyas Sobirin Mohd Sazali. Springer Verlag, 2019. pp. 27-33 (Lecture Notes in Electrical Engineering).

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title = "Enhanced simulated annealing for constrained design problems",

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.",

author = "Hussein Samma and Junita Mohamad-Saleh and Suandi, {Shahrel Azmin}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; 10th International Conference on Robotic, Vision, Signal Processing and Power Applications, ROVISP 2018 ; Conference date: 14-08-2018 Through 15-08-2018",

year = "2019",

doi = "10.1007/978-981-13-6447-1_4",

language = "English",

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series = "Lecture Notes in Electrical Engineering",

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pages = "27--33",

editor = "{Md Zawawi}, {Mohamad Adzhar} and Teoh, {Soo Siang} and Abdullah, {Noramalina Binti} and {Mohd Sazali}, {Mohd Ilyas Sobirin}",

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Samma, H, Mohamad-Saleh, J & Suandi, SA 2019, Enhanced simulated annealing for constrained design problems. in MA Md Zawawi, SS Teoh, NB Abdullah & MIS Mohd Sazali (eds), 10th International Conference on Robotics, Vision, Signal Processing and Power Applications - Enabling Research and Innovation Towards Sustainability. Lecture Notes in Electrical Engineering, vol. 547, Springer Verlag, pp. 27-33, 10th International Conference on Robotic, Vision, Signal Processing and Power Applications, ROVISP 2018, Penang, Malaysia, 14/08/18. https://doi.org/10.1007/978-981-13-6447-1_4

Enhanced simulated annealing for constrained design problems. / Samma, Hussein; Mohamad-Saleh, Junita; Suandi, Shahrel Azmin.
10th International Conference on Robotics, Vision, Signal Processing and Power Applications - Enabling Research and Innovation Towards Sustainability. ed. / Mohamad Adzhar Md Zawawi; Soo Siang Teoh; Noramalina Binti Abdullah; Mohd Ilyas Sobirin Mohd Sazali. Springer Verlag, 2019. p. 27-33 (Lecture Notes in Electrical Engineering; Vol. 547).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Samma, Hussein

AU - Mohamad-Saleh, Junita

AU - Suandi, Shahrel Azmin

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2019.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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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Samma H, Mohamad-Saleh J, Suandi SA. Enhanced simulated annealing for constrained design problems. In Md Zawawi MA, Teoh SS, Abdullah NB, Mohd Sazali MIS, editors, 10th International Conference on Robotics, Vision, Signal Processing and Power Applications - Enabling Research and Innovation Towards Sustainability. Springer Verlag. 2019. p. 27-33. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-13-6447-1_4

Enhanced simulated annealing for constrained design problems

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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Cite this