Volume 9, Issue 2 (June 2024)
J Environ Health Sustain Dev 2024, 9(2): 2249-2256 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Fallah Madvari R, Niknam Sharak M, Jafari M J, Abbasi Balochkhaneh F. Comparing Optimized Sound Absorption Coefficient of Aluminum Foam with Local Search Algorithm, Genetic Algorithm, and Particle Swarm Optimization. J Environ Health Sustain Dev 2024; 9 (2) :2249-2256
URL: http://jehsd.ssu.ac.ir/article-1-692-en.html
URL: http://jehsd.ssu.ac.ir/article-1-692-en.html
Rohollah Fallah Madvari 
, Mohsen Niknam Sharak * , Mohammad Javad Jafari , Faezeh Abbasi Balochkhaneh
, Mohsen Niknam Sharak * , Mohammad Javad Jafari , Faezeh Abbasi Balochkhaneh
PhD Graduate in Mechanical Engineering, University of Birjand, Birjand, Iran
Full-Text [PDF 682 kb]
(135 Downloads)
| Abstract (HTML) (326 Views)
.JPG)
Figure 1: A comparative analysis of the AC of three GLS, GA and PSO models at thicknesses of 5 to 40 mm at different frequencies.
.JPG)
Figure 2: Comparison of AC regarding three GLS, GA, and PSO in frequencies from 500 to 3000 Hz with different thicknesses
Full-Text: (141 Views)
Comparing Optimized Sound Absorption Coefficient of Aluminum Foam with Local Search Algorithm, Genetic Algorithm, and Particle Swarm Optimization
Rohollah Fallah Madvari 1, Mohsen Niknam Sharak 2*, Mohammad Javad Jafari 3, Faezeh Abbasi Balochkhaneh 3
1Industrial Diseases Research Center, Department of Occupational Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
2PhD Graduate in Mechanical Engineering, University of Birjand, Birjand, Iran.
3Department of Occupational Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Citation: Fallah Madvari R, Niknam Sharak M, Jafari MJ, et al. Comparing Optimized Sound Absorption Coefficient of Aluminum Foam with Local Search Algorithm, Genetic Algorithm, and Particle Swarm Optimization. J Environ Health Sustain Dev. 2024; 9(2): 2249-56.
Rohollah Fallah Madvari 1, Mohsen Niknam Sharak 2*, Mohammad Javad Jafari 3, Faezeh Abbasi Balochkhaneh 3
1Industrial Diseases Research Center, Department of Occupational Health Engineering, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
2PhD Graduate in Mechanical Engineering, University of Birjand, Birjand, Iran.
3Department of Occupational Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| A R T I C L E I N F O | ABSTRACT | ||
| ORIGINAL ARTICLE | Introduction: The principle of passive sound control is based on the phenomenon of sound absorption by absorbers. The factors affecting sound absorption include porosity, pore size, pore opening size, thickness, and air flow resistance. Materials and Methods: In this study, the authors compared the optimization results of the effective parameters on sound absorption coefficient (AC) using the three optimization methods: Guided Local Search (GLS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The programming was done in MATLAB software. Thicknesses of 5, 10, 20, 30 and 40 mm were chosen for optimization at frequencies of 500 to 3000 Hz. Results: In frequencies above 2 kHz (thickness 5 to 40 mm), the three optimal methods had the same performance and estimated AC of 1. At low frequencies of 2 kHz and thicknesses of 30 and 40 mm, GA and PSO methods obtained an AC of 1. Conclusion: It seems that the GA and PSO optimization algorithm are suitable methods to optimize the AC of metal foam in low and high frequencies. |
||
Article History: Received: 12 March 2024 Accepted: 20 May 2024 |
|||
*Corresponding Author: Mohsen Niknam Sharak Email: mohsen.niknam@gmail.com Tel: +98 936 4342414 |
|||
Keywords: Sound Absorption, High-Frequency, Low-Frequency, Optimization Algorithm. |
Introduction
One of the sound engineering and technical control methods is controlling based on absorption and insulation1. This control reduces the overall sound by increasing the surface absorption coefficient2. By absorbing sound and preventing its transmission, it leads to an increase in the comfort of people in the work environment. With the advancement of technology, obtaining new materials can create a great effect in the wide and new industrial space, which can be mentioned as one of these materials, metal foam based on aluminum. One of the properties of this foam is sound absorption coefficient (AC) and its features include low density, high mechanical resistance, resistance to high heat and corrosion3. Among the factors affecting AC by aluminum foam, the authors can mention porosity, pore size, pore opening size, thickness, air flow resistance, and pore morphology4. In a study by Shen et al., the results showed that metal foam has a good sound absorption capacity5. Whereas, the important point is to investigate the cellular structure of the foam and achieve the maximum state of the AC; in this direction, laboratory methods and optimization algorithms can be used.
But, it should be noted that the laboratory method is difficult and requires cost, time, and laboratory facilities. As a result, optimization algorithms can be a more suitable option. The purpose of optimization is to find the best acceptable solution, according to the constraints and needs of the problem6. Among the optimization algorithms, the meta-heuristic algorithm can be mentioned, which includes particle swarm optimization (PSO), genetic algorithm (GA), guided local search (GLS), differential evolution (DE), ant harmony search and etc 7. Also, meta-heuristic algorithms are widely used in engineering optimization8. Local search algorithms move from one solution to another in a space of forward solutions using finite variations. Also, its purpose is to find the best mode based on the objective function9. Among the advantages and disadvantages of this local search algorithm, the researchers can respectively reach acceptable solutions in infinite space and stop them at local optimal points10. One of the advantages of the GA is its parallelism, which allows for the simultaneous consideration of multiple starting points for the problem, as well as the exploration of the problem space from multiple directions11. Moreover, among its disadvantages, GA can be used to investigate the problem space globally, and its local search is weak12.
GLS is a metaheuristic computational method for solving complex optimization problems using a solution that maximizes the criterion among multiple solutions13. GLS starts with a proposed solution and attempts to find the best solution within the specific context of the current solution. If a better solution is found, the current solution is replaced, and the process continues from that solution13.
The PSO algorithm is employed in numerous optimization systems due to its reduced memory requirements and high computational efficiency14. Furthermore, it’s implementation and execution are more straightforward than those of other meta-heuristic algorithms. This algorithm has been successfully applied to numerous problems, including the identification of optimal points on standard criterion functions, the resolution of permutation problems, and the training of neural networks14.
The researchers who employed these algorithms in their studies were Wang, Broghany Bonfiglio and Li 15-18. Moreover, among the studies that focused on optimising certain effective parameters in AC were those of Chang et al. 19 and Heidi et al. 20 pointed out that they optimized the thickness of the plate and the MPP structure of multilayer absorbers, respectively. Therefore, considering the importance of the parameters affecting the AC, the authors decided to conduct a study with the aim of comparing the methods of optimizing the parameters affecting the AC of aluminum metal foam with three methods of GLS, GA and PSO.
Materials and Methods
The Lu model was invented by Lu et al. in 200021.
In this model, the values of the variables are changed in a certain interval and the optimal value of the variables is found. The code of the Lu model selects the best solution from all possible values of the parameters21.
In a previous our study, the benchmarking method was used to validate the coding of Lu equations, and the value of R2 was 0.922. The number of intervals for optimization was done by past studies according to the Table 1.
One of the sound engineering and technical control methods is controlling based on absorption and insulation1. This control reduces the overall sound by increasing the surface absorption coefficient2. By absorbing sound and preventing its transmission, it leads to an increase in the comfort of people in the work environment. With the advancement of technology, obtaining new materials can create a great effect in the wide and new industrial space, which can be mentioned as one of these materials, metal foam based on aluminum. One of the properties of this foam is sound absorption coefficient (AC) and its features include low density, high mechanical resistance, resistance to high heat and corrosion3. Among the factors affecting AC by aluminum foam, the authors can mention porosity, pore size, pore opening size, thickness, air flow resistance, and pore morphology4. In a study by Shen et al., the results showed that metal foam has a good sound absorption capacity5. Whereas, the important point is to investigate the cellular structure of the foam and achieve the maximum state of the AC; in this direction, laboratory methods and optimization algorithms can be used.
But, it should be noted that the laboratory method is difficult and requires cost, time, and laboratory facilities. As a result, optimization algorithms can be a more suitable option. The purpose of optimization is to find the best acceptable solution, according to the constraints and needs of the problem6. Among the optimization algorithms, the meta-heuristic algorithm can be mentioned, which includes particle swarm optimization (PSO), genetic algorithm (GA), guided local search (GLS), differential evolution (DE), ant harmony search and etc 7. Also, meta-heuristic algorithms are widely used in engineering optimization8. Local search algorithms move from one solution to another in a space of forward solutions using finite variations. Also, its purpose is to find the best mode based on the objective function9. Among the advantages and disadvantages of this local search algorithm, the researchers can respectively reach acceptable solutions in infinite space and stop them at local optimal points10. One of the advantages of the GA is its parallelism, which allows for the simultaneous consideration of multiple starting points for the problem, as well as the exploration of the problem space from multiple directions11. Moreover, among its disadvantages, GA can be used to investigate the problem space globally, and its local search is weak12.
GLS is a metaheuristic computational method for solving complex optimization problems using a solution that maximizes the criterion among multiple solutions13. GLS starts with a proposed solution and attempts to find the best solution within the specific context of the current solution. If a better solution is found, the current solution is replaced, and the process continues from that solution13.
The PSO algorithm is employed in numerous optimization systems due to its reduced memory requirements and high computational efficiency14. Furthermore, it’s implementation and execution are more straightforward than those of other meta-heuristic algorithms. This algorithm has been successfully applied to numerous problems, including the identification of optimal points on standard criterion functions, the resolution of permutation problems, and the training of neural networks14.
The researchers who employed these algorithms in their studies were Wang, Broghany Bonfiglio and Li 15-18. Moreover, among the studies that focused on optimising certain effective parameters in AC were those of Chang et al. 19 and Heidi et al. 20 pointed out that they optimized the thickness of the plate and the MPP structure of multilayer absorbers, respectively. Therefore, considering the importance of the parameters affecting the AC, the authors decided to conduct a study with the aim of comparing the methods of optimizing the parameters affecting the AC of aluminum metal foam with three methods of GLS, GA and PSO.
Materials and Methods
The Lu model was invented by Lu et al. in 200021.
In this model, the values of the variables are changed in a certain interval and the optimal value of the variables is found. The code of the Lu model selects the best solution from all possible values of the parameters21.
In a previous our study, the benchmarking method was used to validate the coding of Lu equations, and the value of R2 was 0.922. The number of intervals for optimization was done by past studies according to the Table 1.
Table 1: The selected range of parameters affecting the AC optimization
.JPG)
Genetic algorithm (GA)
In a previous our study, the GA method was used for optimization22.
The GA is inspired by genetics and Darwin's theory of evolution and is based on the principle of natural selection, whereby the fittest survive 23. In this algorithm, it performs automatic operations on a set of population (randomly) which has the same chromosome24. In a previous our study, in order to use this algorithm, the researchers created the initial population randomly by entering the parameters of the problem22. Then, they performed the crossover on the parents and the mutation and evaluated the answers and determined the best answer and the variables corresponding to it22. It should be noted that if the stop condition is met, the next step is implemented.
Local search algorithm (GLS)
In a previous our study, the GLS method was used for optimization13.
GLS are widely used in a large number for hard computational problems, including computer science, especially artificial intelligence, mathematics, and engineering25. GLS is a modification that focuses on reaching the goal, and in this algorithm, the path and the cost of reaching are less considered. Also, this algorithm can be used as finding a solution to maximize a criterion among a number of possible solutions. This algorithm moves from one solution to another in a space of future solutions with the benefit of limited changes until a suitable solution is found. In a previous our study, in order to use this algorithm, the numerical solution of the Lu model problem was first addressed for validation13. In the second and third stages, coding was done with the GLS method using MATLAB software and determining the value of the optimal parameters13.
Particle swarm algorithm (PSO)
In a previous our study, the PSO method was used for optimization 26.
PSO algorithm is an optimization method based on probability rules27. It was first coined in 1995 by Kennedy and Eberhart, inspired by the behavior of birds when searching for food. In this algorithm, first a set of initial answers is generated and then searching for the answer is done to find the optimal answer in the space of possible answers, or to time the generations. Each particle is defined multidimensionally with two values of position and velocity, and at each stage of the particle's movement, with two indices of velocity and position, the best responses are determined for all particles in terms of competence. Finding the best solution in the field of feature selection according to the global search strategy is one of the most important advantages of PSO 27.
Results
In GA, PSO and GLS, three physical parameters are optimized at different frequencies. The results of optimized AC using GLS, PSO and GA have been compared in thicknesses of 5, 10, 20, 30 and 40 mm. In this study, the low frequency is under 2 KHz, and the high frequency is above 2 KHz.
Figure 1 compares three optimization methods in thicknesses of 5 and 10 mm considering different ACs. According to figure 1a, three optimal methods with increasing frequency the AC is closer to 1. The increasing the thickness, the higher the AC is at low frequencies. The GLS method has a maximum thickness of 10 mm. Also, at frequencies under 2 KHz, the AC is less than 1.
In figure 1b, GA and PSO have the maximum AC at low frequency in 30 mm thickness. This is while the GA has the minimum AC at frequencies under 2 kHz in the thickness of 20 mm.
Figure 1c (40 mm thickness) indicated that the GA and PSO have the highest absorption in all frequencies, but the GLS method shows an AC of 0.8 at low frequencies.
Figure 2a showed the comparison of three methods at a low frequency of 500 Hz and in different thicknesses, where the GA method and PSO have an AC of 1 in thicknesses of 40 mm. As a result, it can be stated that GA and PSO have shown better performance with increasing thickness.
Figure 2b at a frequency of 1000 Hz showed that the GLS and PSO methods in thicknesses of 20, 30 and 40 mm have AC close to 1. In the GA method in thicknesses of 30 and 40 mm, the AC is close to 1.
Figure 2C at a frequency of 2000 Hz showed that the AC of the GLS method is close to 1 in the thicknesses of 5 to 40 mm. The AC of the GA and PSO in the thicknesses of 10 to 40 mm is close to 1.
Figure 2d at a frequency of 3000 Hz showed that the AC of the GLS method in thicknesses of 5, 10, 20 and 40 mm is close to 1. In the method of GA and PSO in thicknesses of 5 to 40 mm, AC is close to 1. In the thickness of 30 mm, a significant reduction is observed in the GLS method.
In a previous our study, the GA method was used for optimization22.
The GA is inspired by genetics and Darwin's theory of evolution and is based on the principle of natural selection, whereby the fittest survive 23. In this algorithm, it performs automatic operations on a set of population (randomly) which has the same chromosome24. In a previous our study, in order to use this algorithm, the researchers created the initial population randomly by entering the parameters of the problem22. Then, they performed the crossover on the parents and the mutation and evaluated the answers and determined the best answer and the variables corresponding to it22. It should be noted that if the stop condition is met, the next step is implemented.
Local search algorithm (GLS)
In a previous our study, the GLS method was used for optimization13.
GLS are widely used in a large number for hard computational problems, including computer science, especially artificial intelligence, mathematics, and engineering25. GLS is a modification that focuses on reaching the goal, and in this algorithm, the path and the cost of reaching are less considered. Also, this algorithm can be used as finding a solution to maximize a criterion among a number of possible solutions. This algorithm moves from one solution to another in a space of future solutions with the benefit of limited changes until a suitable solution is found. In a previous our study, in order to use this algorithm, the numerical solution of the Lu model problem was first addressed for validation13. In the second and third stages, coding was done with the GLS method using MATLAB software and determining the value of the optimal parameters13.
Particle swarm algorithm (PSO)
In a previous our study, the PSO method was used for optimization 26.
PSO algorithm is an optimization method based on probability rules27. It was first coined in 1995 by Kennedy and Eberhart, inspired by the behavior of birds when searching for food. In this algorithm, first a set of initial answers is generated and then searching for the answer is done to find the optimal answer in the space of possible answers, or to time the generations. Each particle is defined multidimensionally with two values of position and velocity, and at each stage of the particle's movement, with two indices of velocity and position, the best responses are determined for all particles in terms of competence. Finding the best solution in the field of feature selection according to the global search strategy is one of the most important advantages of PSO 27.
Results
In GA, PSO and GLS, three physical parameters are optimized at different frequencies. The results of optimized AC using GLS, PSO and GA have been compared in thicknesses of 5, 10, 20, 30 and 40 mm. In this study, the low frequency is under 2 KHz, and the high frequency is above 2 KHz.
Figure 1 compares three optimization methods in thicknesses of 5 and 10 mm considering different ACs. According to figure 1a, three optimal methods with increasing frequency the AC is closer to 1. The increasing the thickness, the higher the AC is at low frequencies. The GLS method has a maximum thickness of 10 mm. Also, at frequencies under 2 KHz, the AC is less than 1.
In figure 1b, GA and PSO have the maximum AC at low frequency in 30 mm thickness. This is while the GA has the minimum AC at frequencies under 2 kHz in the thickness of 20 mm.
Figure 1c (40 mm thickness) indicated that the GA and PSO have the highest absorption in all frequencies, but the GLS method shows an AC of 0.8 at low frequencies.
Figure 2a showed the comparison of three methods at a low frequency of 500 Hz and in different thicknesses, where the GA method and PSO have an AC of 1 in thicknesses of 40 mm. As a result, it can be stated that GA and PSO have shown better performance with increasing thickness.
Figure 2b at a frequency of 1000 Hz showed that the GLS and PSO methods in thicknesses of 20, 30 and 40 mm have AC close to 1. In the GA method in thicknesses of 30 and 40 mm, the AC is close to 1.
Figure 2C at a frequency of 2000 Hz showed that the AC of the GLS method is close to 1 in the thicknesses of 5 to 40 mm. The AC of the GA and PSO in the thicknesses of 10 to 40 mm is close to 1.
Figure 2d at a frequency of 3000 Hz showed that the AC of the GLS method in thicknesses of 5, 10, 20 and 40 mm is close to 1. In the method of GA and PSO in thicknesses of 5 to 40 mm, AC is close to 1. In the thickness of 30 mm, a significant reduction is observed in the GLS method.
Figure 1: A comparative analysis of the AC of three GLS, GA and PSO models at thicknesses of 5 to 40 mm at different frequencies.
Figure 2: Comparison of AC regarding three GLS, GA, and PSO in frequencies from 500 to 3000 Hz with different thicknesses
The two methods of GA and PSO were suitable for optimizing the porosity parameter, pore size, and pore opening size at low frequency (500 Hz) and thickness of 40 mm.
Discussion
Past studies used GA to optimize the design of absorbents. Broghany et al. in 2016, considered GA as an effective tool in optimizing the design of three-layer porous absorbent. The optimization results of their study showed that the three-layer porous structure with a thickness of less than one-fifth of the wavelength has better performance18. Also, the study by Lim et al. investigated and compared three methods of GA, differential evolution (DE), and PSO for optimization, the results of which indicated that GA is faster than any other in obtaining the highest number28.
In this regard, other studies have also introduced GA as a success factor in many optimization problems, and this algorithm makes a more suitable choice to be made by searching for countless solutions from the problem area29. One of the advantages of the GLS is that it consumes little memory, which is usually a constant value can search for acceptable solutions in a large space, and obtain a favorable result30. Also, this algorithm can always provide a reliable solution, even if it is interrupted at any time31.
The results of the present study showed that in three optimization methods, the AC increases with the increase of thickness at high frequency.
It can be reasonably assumed that an increase in absorber thickness will result in a corresponding increase in AC peak. It is understood that due to the long propagation distance in relatively thick samples, there is an increased interaction of the sound wave with the pore walls.
According to the studies, porous metal foam shows higher sound absorption behaviors than non-optimal samples at different frequencies using optimal morphology 32. The study by Hakamada et al. 33 was also similar to this study. These studies stated that lack of apparent correlation between the pore size and the AC is due to the significant effects of the pore opening size, which is similar to the results of this study. Moreover, Navacerrada et al. 34 demonstrated that the cell diameter of metal foams stabilizes the properties of these materials, and the foams produced by infiltration technique are characterized by a very homogeneous structure and a high AC at low frequencies. In general, aluminum foam with a diameter of 0.5 mm is a good choice for applications. Through optimization design, in high frequency spectrum and medium, PM has better sound absorption properties. However, it has a weak absorption effect at low frequency. Therefore, in this study, it was stated that according to the actual sound spectrum, appropriate sound absorption materials should be selected15.
Conclusion
In this study, the optimization tools of acoustic absorption parameters of porous metal materials are presented, which include GA, GLS, and PSO. The comparison of the present study with the results of the Lu model has confirmed the theoretical model in evaluating the performance of AC in the case of porous metal foam. The Lu model can be used as a tool for optimization of porous structures. The performance of GA and PSO is better than GLS at 500 Hz frequency, and at frequency of 1000 Hz, GLS and PSO have an AC of close to 1 in thickness of 20, 30, and 40 mm, respectively. This is while the AC is close to 1 in the 2 kHz frequencies of the three methods. In the thickness of 10 to 40 mm, and at the frequency of 3000 Hz, GA and PSO perform better than the GLS.
Acknowledgement
The authors would like to thank Shahid Sadoughi University of Medical Sciences for their technical and financial support (Project code: 11393).
Conflict of Interest
The authors declared no conflict of interests.
Funding
This study was financially supported by Shahid Sadoughi University of Medical Sciences.
Code of Ethics
The study was approved by the university’s Ethics Committee with ethics code of IR.SSU.SRH.REC.1400.022.
Authors' contributions
The study’s conception and design was carried out by Rohollah Fallah Madvari and Mohsen Niknam Sharak; data collection by Mohammad Javad Jafari and Faeza Abbasi; analysis and interpretation of results by Rohollah Fallah Madvari, Mohsen Niknam Sharak, and Mohammad Javad Jafari; drafting the manuscript by Rohollah Fallah Madvari, Mohsen Niknam Sharak, Mohammad Javad Jafari, and Faeza Abbasi. All the authors reviewed the results and approved the final version of the manuscript.
This is an Open-Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt, and build upon this work for commercial use.
Reference
1. Hajizadeh R, Khavanin A, Jonidi Jafari A, et al. Investigation of acoustic properties of polymer nanocomposites regarding combined sound absorption and insulation characteristics. Journal of Health and Safety at Work. 2019;9(4):311-28.
2. Arunkumar M, Pitchaimani J, Gangadharan K, et al. Sound transmission loss characteristics of sandwich aircraft panels: influence of nature of core. Journal of Sandwich Structures & Materials. 2017;19(1):26-48.
3. Campana F, Pilone D. Effect of heat treatments on the mechanical behaviour of aluminium alloy foams. Scr Mater. 2009;60(8):679-82.
4. Bahreini A, Alizadeh A, Baharvandi H, et al. Investigation of the effect of aluminum alloy foam pore size on the sound absorption coefficient. Journal of Acoustical Society of Iran. 2015;2(2):34-40.
5. Shen X, Bai P, Chen L, et al. Development of thin sound absorber by parameter optimization of multilayer compressed porous metal with rear cavity. Applied Acoustics. 2020;159:107071.
6. Soupios P, Akca I, Mpogiatzis P, et al. Applications of hybrid genetic algorithms in seismic tomography. J Appl Geophy. 2011;75(3):479-89.
7. Gonzalez TF. Handbook of approximation algorithms and metaheuristics: Chapman and Hall/CRC; 2007.
8. Al-Salt J. A comparsion between genetic algorithms and sequential quadratic programming in solving constrained optimization problems. Advances in Modal Logic Journal. 2006;6:67-74.
9. Gonçalves JF, de Magalhães Mendes JJ, Resende McG. A hybrid genetic algorithm for the job shop scheduling problem. Eur J Oper Res. 2005;167(1):77-95.
10. Kelner V, Capitanescu F, Léonard O, et al. A hybrid optimization technique coupling an evolutionary and a local search algorithm. J Comput Appl Math. 2008;215(2):448-56.
11. Harada T, Alba E. Parallel genetic algorithms: a useful survey. ACM Computing Surveys (CSUR). 2020;53(4):1-39.
12. Mirjalili S. Evolutionary algorithms and neural networks. Studies in computational intelligence. 2019;780.
13. Jafari MJ, Khavanin A, Ebadzadeh T, et al. Optimization of the morphological parameters of a metal foam for the highest sound absorption coefficient using local search algorithm. Archives of Acoustics. 2020;45(3):487-97.
14. Amoshahy MJ, Shamsi M, Sedaaghi MH. An improved particle swarm optimizer based on a novel class of fast and efficient learning factors strategies. Journal of Nonlinear Systems in Electrical Engineering. 2020;6(1):4-33.
15. Wang Y, Liu S, Wu H, et al. On-demand optimize design of sound-absorbing porous material based on multi-population genetic algorithm. e-Polymers. 2020;20(1):122-32.
16. Bonfiglio P, Pompoli F. Inversion problems for determining physical parameters of porous materials: overview and comparison between different methods. Acta Acustica united with Acustica. 2013;99(3):341-51.
17. Li Z, Yu L, Trzasko JD, et al. Adaptive nonlocal means filtering based on local noise level for CT denoising. Medical physics. 2014;41(1):011908.
18. Broghany M, Basirjafari S, Saffar S. Optimization of flat multi-layer sound absorber by using multi-objective genetic algorithm for application in anechoic chamber. Modares Mechanical Engineering. 2016;16(2):215-22.
19. Chang YC, Yeh LJ, Chiu MC. Optimization of constrained composite absorbers using simulated annealing. Applied Acoustics. 2005;66(3):341-52.
20. Ruiz H, Cobo P, Jacobsen F. Optimization of multiple-layer microperforated panels by simulated annealing. Applied Acoustics. 2011;72(10):772-6.
21. Lu TJ, Chen F, He D. Sound absorption of cellular metals with semiopen cells. J Acoust Soc Am. 2000;108(4):1697-709.
22. Jafari MJ, Sharak MN, Khavanin A, et al. Improving the cellular characteristics of aluminum foam for maximum sound absorption coefficient using genetic algorithm. Sound & Vibration. 2021;55(2).
23. Lu T, Zhu J. A genetic algorithm for finding a path subject to two constraints. Appl Soft Comput. 2013;13(2):891-8.
24. Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation. 2002;6(2):182-97.
25. Cohen-Addad V, Mathieu C. The unreasonable success of local search: geometric optimization. Arxiv. preprint arXiv:1410.0553. 2014 Oct 2.
26. Madvari RF, Sharak MN, Tehrani MJ, et al. Estimation of metal foam microstructure parameters for maximum sound absorption coefficient in specified frequency band using particle swarm optimisation. Archives of Acoustics. 2022:33-42.
27. Rafiei A, Moradi P, Ghaderzadeh A. Multi-label feature selection using a hybrid approach based on the particle swarm optimization algorithm. Nashriyyah -I Muhandisi -I Barq Va Muhandisi -I Kampyutar -I Iran, B- Muhandisi -I Kampyutar[Internet]. 2023;20(4):271-281. Available from: https://sid.ir/paper/1033096/en. [cited Oct 20, 2023].
28. Lim SP, Haron H, editors. Performance comparison of genetic algorithm, differential evolution and particle swarm optimization towards benchmark functions. In 2013 IEEE Conference on Open Systems (ICOS) 2013 Dec 2 (pp. 41-46). IEEE.
29. Garg P. A comparison between memetic algorithm and genetic algorithm for the cryptanalysis of simplified data encryption standard algorithm. Arxiv. preprint arXiv:1004.0574. 2010 Apr 5.
30. Bibel W, Jorrand P. Fundamentals of artificial intelligence: an advanced course. Springer- Verlag; 1986 May 1.
31. Liberatore F, Camacho-Collados M. A comparison of local search methods for the multicriteria police districting problem on graph. Mathematical Problems in Engineering. 2016;2016(1):3690474.
32. Li Y, Wang X, Wang X,et al. Sound absorption characteristics of aluminum foam with spherical cells. Journal of Applied Physics. 2011;110(11).
33. Hakamada M, Kuromura T, Chen Y, et al. Sound absorption characteristics of porous aluminum fabricated by spacer method. Journal of Applied Physics. 2006;100(11).
34. Navacerrada M, Fernández P, Díaz C,
et al. Thermal and acoustic properties of aluminium foams manufactured by the infiltration process. Applied Acoustics. 2013;74(4):496-501.
Discussion
Past studies used GA to optimize the design of absorbents. Broghany et al. in 2016, considered GA as an effective tool in optimizing the design of three-layer porous absorbent. The optimization results of their study showed that the three-layer porous structure with a thickness of less than one-fifth of the wavelength has better performance18. Also, the study by Lim et al. investigated and compared three methods of GA, differential evolution (DE), and PSO for optimization, the results of which indicated that GA is faster than any other in obtaining the highest number28.
In this regard, other studies have also introduced GA as a success factor in many optimization problems, and this algorithm makes a more suitable choice to be made by searching for countless solutions from the problem area29. One of the advantages of the GLS is that it consumes little memory, which is usually a constant value can search for acceptable solutions in a large space, and obtain a favorable result30. Also, this algorithm can always provide a reliable solution, even if it is interrupted at any time31.
The results of the present study showed that in three optimization methods, the AC increases with the increase of thickness at high frequency.
It can be reasonably assumed that an increase in absorber thickness will result in a corresponding increase in AC peak. It is understood that due to the long propagation distance in relatively thick samples, there is an increased interaction of the sound wave with the pore walls.
According to the studies, porous metal foam shows higher sound absorption behaviors than non-optimal samples at different frequencies using optimal morphology 32. The study by Hakamada et al. 33 was also similar to this study. These studies stated that lack of apparent correlation between the pore size and the AC is due to the significant effects of the pore opening size, which is similar to the results of this study. Moreover, Navacerrada et al. 34 demonstrated that the cell diameter of metal foams stabilizes the properties of these materials, and the foams produced by infiltration technique are characterized by a very homogeneous structure and a high AC at low frequencies. In general, aluminum foam with a diameter of 0.5 mm is a good choice for applications. Through optimization design, in high frequency spectrum and medium, PM has better sound absorption properties. However, it has a weak absorption effect at low frequency. Therefore, in this study, it was stated that according to the actual sound spectrum, appropriate sound absorption materials should be selected15.
Conclusion
In this study, the optimization tools of acoustic absorption parameters of porous metal materials are presented, which include GA, GLS, and PSO. The comparison of the present study with the results of the Lu model has confirmed the theoretical model in evaluating the performance of AC in the case of porous metal foam. The Lu model can be used as a tool for optimization of porous structures. The performance of GA and PSO is better than GLS at 500 Hz frequency, and at frequency of 1000 Hz, GLS and PSO have an AC of close to 1 in thickness of 20, 30, and 40 mm, respectively. This is while the AC is close to 1 in the 2 kHz frequencies of the three methods. In the thickness of 10 to 40 mm, and at the frequency of 3000 Hz, GA and PSO perform better than the GLS.
Acknowledgement
The authors would like to thank Shahid Sadoughi University of Medical Sciences for their technical and financial support (Project code: 11393).
Conflict of Interest
The authors declared no conflict of interests.
Funding
This study was financially supported by Shahid Sadoughi University of Medical Sciences.
Code of Ethics
The study was approved by the university’s Ethics Committee with ethics code of IR.SSU.SRH.REC.1400.022.
Authors' contributions
The study’s conception and design was carried out by Rohollah Fallah Madvari and Mohsen Niknam Sharak; data collection by Mohammad Javad Jafari and Faeza Abbasi; analysis and interpretation of results by Rohollah Fallah Madvari, Mohsen Niknam Sharak, and Mohammad Javad Jafari; drafting the manuscript by Rohollah Fallah Madvari, Mohsen Niknam Sharak, Mohammad Javad Jafari, and Faeza Abbasi. All the authors reviewed the results and approved the final version of the manuscript.
This is an Open-Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt, and build upon this work for commercial use.
Reference
1. Hajizadeh R, Khavanin A, Jonidi Jafari A, et al. Investigation of acoustic properties of polymer nanocomposites regarding combined sound absorption and insulation characteristics. Journal of Health and Safety at Work. 2019;9(4):311-28.
2. Arunkumar M, Pitchaimani J, Gangadharan K, et al. Sound transmission loss characteristics of sandwich aircraft panels: influence of nature of core. Journal of Sandwich Structures & Materials. 2017;19(1):26-48.
3. Campana F, Pilone D. Effect of heat treatments on the mechanical behaviour of aluminium alloy foams. Scr Mater. 2009;60(8):679-82.
4. Bahreini A, Alizadeh A, Baharvandi H, et al. Investigation of the effect of aluminum alloy foam pore size on the sound absorption coefficient. Journal of Acoustical Society of Iran. 2015;2(2):34-40.
5. Shen X, Bai P, Chen L, et al. Development of thin sound absorber by parameter optimization of multilayer compressed porous metal with rear cavity. Applied Acoustics. 2020;159:107071.
6. Soupios P, Akca I, Mpogiatzis P, et al. Applications of hybrid genetic algorithms in seismic tomography. J Appl Geophy. 2011;75(3):479-89.
7. Gonzalez TF. Handbook of approximation algorithms and metaheuristics: Chapman and Hall/CRC; 2007.
8. Al-Salt J. A comparsion between genetic algorithms and sequential quadratic programming in solving constrained optimization problems. Advances in Modal Logic Journal. 2006;6:67-74.
9. Gonçalves JF, de Magalhães Mendes JJ, Resende McG. A hybrid genetic algorithm for the job shop scheduling problem. Eur J Oper Res. 2005;167(1):77-95.
10. Kelner V, Capitanescu F, Léonard O, et al. A hybrid optimization technique coupling an evolutionary and a local search algorithm. J Comput Appl Math. 2008;215(2):448-56.
11. Harada T, Alba E. Parallel genetic algorithms: a useful survey. ACM Computing Surveys (CSUR). 2020;53(4):1-39.
12. Mirjalili S. Evolutionary algorithms and neural networks. Studies in computational intelligence. 2019;780.
13. Jafari MJ, Khavanin A, Ebadzadeh T, et al. Optimization of the morphological parameters of a metal foam for the highest sound absorption coefficient using local search algorithm. Archives of Acoustics. 2020;45(3):487-97.
14. Amoshahy MJ, Shamsi M, Sedaaghi MH. An improved particle swarm optimizer based on a novel class of fast and efficient learning factors strategies. Journal of Nonlinear Systems in Electrical Engineering. 2020;6(1):4-33.
15. Wang Y, Liu S, Wu H, et al. On-demand optimize design of sound-absorbing porous material based on multi-population genetic algorithm. e-Polymers. 2020;20(1):122-32.
16. Bonfiglio P, Pompoli F. Inversion problems for determining physical parameters of porous materials: overview and comparison between different methods. Acta Acustica united with Acustica. 2013;99(3):341-51.
17. Li Z, Yu L, Trzasko JD, et al. Adaptive nonlocal means filtering based on local noise level for CT denoising. Medical physics. 2014;41(1):011908.
18. Broghany M, Basirjafari S, Saffar S. Optimization of flat multi-layer sound absorber by using multi-objective genetic algorithm for application in anechoic chamber. Modares Mechanical Engineering. 2016;16(2):215-22.
19. Chang YC, Yeh LJ, Chiu MC. Optimization of constrained composite absorbers using simulated annealing. Applied Acoustics. 2005;66(3):341-52.
20. Ruiz H, Cobo P, Jacobsen F. Optimization of multiple-layer microperforated panels by simulated annealing. Applied Acoustics. 2011;72(10):772-6.
21. Lu TJ, Chen F, He D. Sound absorption of cellular metals with semiopen cells. J Acoust Soc Am. 2000;108(4):1697-709.
22. Jafari MJ, Sharak MN, Khavanin A, et al. Improving the cellular characteristics of aluminum foam for maximum sound absorption coefficient using genetic algorithm. Sound & Vibration. 2021;55(2).
23. Lu T, Zhu J. A genetic algorithm for finding a path subject to two constraints. Appl Soft Comput. 2013;13(2):891-8.
24. Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation. 2002;6(2):182-97.
25. Cohen-Addad V, Mathieu C. The unreasonable success of local search: geometric optimization. Arxiv. preprint arXiv:1410.0553. 2014 Oct 2.
26. Madvari RF, Sharak MN, Tehrani MJ, et al. Estimation of metal foam microstructure parameters for maximum sound absorption coefficient in specified frequency band using particle swarm optimisation. Archives of Acoustics. 2022:33-42.
27. Rafiei A, Moradi P, Ghaderzadeh A. Multi-label feature selection using a hybrid approach based on the particle swarm optimization algorithm. Nashriyyah -I Muhandisi -I Barq Va Muhandisi -I Kampyutar -I Iran, B- Muhandisi -I Kampyutar[Internet]. 2023;20(4):271-281. Available from: https://sid.ir/paper/1033096/en. [cited Oct 20, 2023].
28. Lim SP, Haron H, editors. Performance comparison of genetic algorithm, differential evolution and particle swarm optimization towards benchmark functions. In 2013 IEEE Conference on Open Systems (ICOS) 2013 Dec 2 (pp. 41-46). IEEE.
29. Garg P. A comparison between memetic algorithm and genetic algorithm for the cryptanalysis of simplified data encryption standard algorithm. Arxiv. preprint arXiv:1004.0574. 2010 Apr 5.
30. Bibel W, Jorrand P. Fundamentals of artificial intelligence: an advanced course. Springer- Verlag; 1986 May 1.
31. Liberatore F, Camacho-Collados M. A comparison of local search methods for the multicriteria police districting problem on graph. Mathematical Problems in Engineering. 2016;2016(1):3690474.
32. Li Y, Wang X, Wang X,et al. Sound absorption characteristics of aluminum foam with spherical cells. Journal of Applied Physics. 2011;110(11).
33. Hakamada M, Kuromura T, Chen Y, et al. Sound absorption characteristics of porous aluminum fabricated by spacer method. Journal of Applied Physics. 2006;100(11).
34. Navacerrada M, Fernández P, Díaz C,
et al. Thermal and acoustic properties of aluminium foams manufactured by the infiltration process. Applied Acoustics. 2013;74(4):496-501.
Type of Study: Original articles |
Subject:
Noise and radiation control
Received: 2024/03/12 | Accepted: 2024/05/20 | Published: 2024/06/26
Received: 2024/03/12 | Accepted: 2024/05/20 | Published: 2024/06/26
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
