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Selection of Genetic Algorithm Parameters for Optimization of Storm-Sewer Networks Using Taguchi Method

Year 2024, Volume: 14 Issue: 1, 1 - 10, 29.04.2024

Tülin Çetin Mehmet Ali Ilgın Mehmet Ali Yurdusev

Abstract

Genetic algorithm is one of the most referred optimization techniques for the hydraulic optimization of stormwater and sewer system design. The genetic algorithm has different parameters affecting its performance such as population sizes, crossover methods, crossover rates, elitism rates, mutation rates and comparative values for the tournament selection algorithm. By the nature of the genetic algorithm, different values of these parameters should be tried to get the optimum values for them. But checking all possible values of these parameters that is a full factorial design is too much time consuming. Therefore, in this study, the Taguchi method is proposed for the first time in the literature to determine the most suitable parameter values of the genetic algorithm used for the hydraulic optimization of stormwater and sewage system design. The L16 Taguchi orthogonal array that involves 16 experiments was employed. Each experiment was replicated six times. Only 16x6=96 runs were carried out to determine the significance of the factors instead of running a full factorial design with 44x6=1536 runs. As a result, the cost value obtained with the Taguchi method is very close (0,15 %) to the cost value obtained with the full factorial design. Moreover, with the Taguchi method, the results are obtained in a shorter time (7 days instead of 4 months) with much fewer attempts.

Keywords

Storm-sewer networks optimization genetic algorithm taguchi

References

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Yağmur Suyu-Kanalizasyon Şebekelerinin Optimizasyonunda Taguchi Yöntemi ile Genetik Algoritma Parametrelerinin Seçimi

Year 2024, Volume: 14 Issue: 1, 1 - 10, 29.04.2024

Tülin Çetin Mehmet Ali Ilgın Mehmet Ali Yurdusev

Abstract

Genetik algoritma, yağmursuyu ve kanalizasyon sistem tasarımının hidrolik optimizasyonu için en çok başvurulan optimizasyon tekniklerinden biridir. Genetik algoritma, performansını etkileyen popülasyon büyüklüğü, çaprazlama yöntemleri, çaprazlama oranları, elitizm oranları, mutasyon oranları ve turnuva seçim algoritması için karşılaştırma değerleri gibi farklı parametrelere sahiptir. Genetik algoritmanın doğası gereği bu parametrelerin optimum değerlerini elde etmek için, parametrelerin farklı değerleri denenmelidir. Fakat tam faktöriyel tasarım olan bu parametrelerin tüm olası değerlerini denemek, çok fazla zaman alır. Bu nedenle, yağmursuyu ve kanalizasyon sistem tasarımının hidrolik optimizasyonu için kullanılan genetik algoritmanın en uygun parametrelerini belirlemek için Taguchi Yöntemi, literatürde ilk defa bu çalışmada önerilmektedir. Taguchi yönteminin 16 deney anlamına gelen L16 ortogonal dizisi kullanılmıştır. Her deney altı kez tekrarlanmıştır. Faktörlerin anlamlılığını belirlemek için tam faktöriyel tasarımda 44x6=1536 defa çalıştırma yerine sadece 16x6=96 defa çalıştırılmıştır. Sonuç olarak, Taguchi yöntemiyle elde edilen maliyet değeri, tam faktöriyel tasarımla elde edilen maliyet değerine oldukça yakındır (0,15 %). Üstelik Taguchi yöntemi ile çok daha az denemeyle daha kısa sürede (4 ay yerine 7 gün) sonuç elde edilmektedir.

Keywords

Yağmur suyu-kanalizasyon şebekeleri optimizasyon genetik algoritma taguchi

References

  • Afshar, M.H. 2006. Application of a genetic algorithm to storm sewer network optimization. Scientia Iranica, Vol. 13, No. 3, pp 234-244.
  • Afshar, M.H. 2008. Rebirthing Particle Swarm Optimization Algorithm: Application to Storm Water Network Design. Canadian Journal of Civil Engineering 35:1120-1127. Doi: 10.1139/L08-056.
  • Afshar, M.H. 2010. A Parameter Free Continuous Ant Colony Optimization Algorithm for the Optimal Design of Storm Sewer Networks: Constrained and Unconstrained Approach. Advances in Engineering Software 41 188–195. Doi: 10.1016/j.advengsoft.2009.09.009.
  • Afshar, M.H. 2012. Rebirthing genetic algorithm for storm sewer network design. Scientia Iranica A, 19 (1), 11–19. Doi: 10.1016/j.scient.2011.12.005
  • Afshar, M.H., Afshar, A., Mariño, M.A., Darbandi, A.A.S. 2006. Hydrograph-based storm sewer design optimization by genetic algorithm. Can. J. Civ. Eng. 33: 319–325. Doi: 10.1139/L05-121
  • Akkuş, H., Yaka, H. 2018. Optimization of turning process by using Taguchi method. Sakarya University Journal of Science, 22 (5), 1444-1448. Doi: 10.16984/saufenbilder
  • Bayrak, O.Ü., Hınıslıoğlu, S. 2013. Investigation of compressive strength of pavement concrete by the Taguchi method. EÜFBED - Fen Bilimleri Enstitüsü Dergisi Cilt-Sayı: 6-1 99-110.
  • Brand, N., Ostfeld, A. 2011. Optimal design of regional wastewater pipelines and treatment plant systems. Water Environment Research, 83, 53-64. Doi: 10.2175/106143010X12780288628219
  • Cetin, T., Yurdusev, M.A. 2017. Genetic algorithm for networks with dynamic mutation rate. Gradevinar, 69 (12), 1101-1109. Doi: 10.14256/JCE.1533.2015
  • Cetin, T., Turan, M.E. 2022. Kanalizasyon Şebekesi Optimizasyonunda Popülasyon Boyutunun Guguk Kuşu Arama Algoritması Üzerine Etkileri [Effects of Population Size on Cuckoo Search Algorithm in Sewer]. Karabakh III. International Congress of Applied Sciences, Proceedings Book. Volume-I, Page: 60-72, June 7-10.
  • Chanda, A., Bhattacharyya, D. 2021. A parametric study to minimise spring-back while producing plywood channels. Journal of Cleaner Production 304 127109. Doi: 10.1016/j.jclepro.2021.127109
  • Chen, H.-J., Lin, H.-C., Tang, C.-W. 2021. Application of the Taguchi method for optimizing the process parameters of producing controlled low-strength materials by using dimension stone sludge and lightweight aggregates. Sustainability, 13, 5576. Doi: 10.3390/su13105576 Cimorelli, L., Cozzolino, L., Covelli, C., Mucherino, C., Palumbo, A., Pianese, D. 2013. Optimal design of rural drainage networks. Journal of Irrigation and Drainage Engineering, 139(2):137-144. Doi: 10.1061/(ASCE)IR.1943-4774.0000526
  • Duque, N., Duque, D., Aguilar, A., Saldarriaga, J. 2020. Sewer Network Layout Selection and Hydraulic Design Using a Mathematical Optimization Framework. Water. 12, 3337. Doi: 10.3390/w12123337
  • Ekmekcioğlu, Ö., Başakın, E.E., Özger, M., 2023. Exploring the practical application of genetic programming for stormwater drain inlet hydraulic efficiency estimation. International Journal of Environmental Science and Technology 20:1489–1502. Doi: 10.1007/s13762-022-04035-9
  • El-Haik, B.S., Shaout, 2010. A Software design for six sigma: a roadmap for excellence. New Jersey: John Wiley & Sons. Doi: 10.1002/9780470877845
  • Elsheikh, A.H., Muthuramalingam, T., Abd Elaziz, M., Ibrahim,·A.M.M., Showaib, E.A. 2021. Minimization of fume emissions in laser cutting of polyvinyl chloride sheets using genetic algorithm. International Journal of Environmental Science and Technology. Doi: 10.1007/s13762-021-03566-x
  • George, A.M., Tembhurkar, A.R. 2020. Taguchi experimental design for adsorptive removal of fluoride from water using novel Ficus Glomerata Bark‑developed biosorbent. International Journal of Environmental Science and Technology, 17:4829–4840. Doi: 10.1007/s13762-020-02787-w
  • Gholizadeh-Tayyar, S., Okongwu, U., Lamothe, J. 2021. A heuristic-based genetic algorithm for scheduling of multiple projects subjected to resource constraints and environmental responsibility commitments. Process Integration and Optimization for Sustainability 5:361–382. Doi: 10.1007/s41660-020-00150-7
  • Gisbert, C.M., Lozano-Galant, J.A., Paya-Zaforteza, I., Turmo, J. 2020. Calibration of the descent local search algorithm parameters using orthogonal arrays. Computer-Aided Civil and Infrastructure Engineering. 35(9):997-1008. Doi: 10.1111/mice.12545
  • Gologlu, C., Sakarya, N. 2008. The effects of cutter path strategies on surface roughness of pocket milling of 1.2738 steel based on Taguchi method. Journal Of Materials Processing Technology. 206 7–15. Doi: 10.1016/j.jmatprotec.2007.11.300
  • Guo, Y., Walters, G.A., Khu, S.T., Keedwell, E.C. 2006. Optimal design of sewer networks using hybrid cellular automata and genetic algorithm. IWA Publishing.
  • Guo, Y., Walters, G.A., Khu, S.T., Keedwell, E. 2007. A Novel Cellular Automata Based Approach to Storm Sewer Design. Engineering Optimization 39:3, 345-364. Doi: 10.1080/03052150601128261.
  • Guo, Y., Walters, G.A., Khu, S.T., Keedwell, E.C. 2008. Efficient multiobjective storm sewer design using cellular automata and genetic algorithm hybrid. Journal of Water Resources Planning and Management, Vol. 134, No. 6. Doi: 10.1061/(ASCE)0733-9496(2008)134:6(511)
  • Haghighi, A., Bakhshipour, A.E. 2012. Optimization of sewer networks using an adaptive genetic algorithm. Water Resource Management, 26:3441–3456. Doi: 10.1007/s11269-012-0084-3
  • Haghighi, A. 2013. Loop-by-Loop Cutting Algorithm to Generate Layouts for Urban Drainage Systems. Journal of Water Resources Planning and Management Vol. 139, No. 6. Doi: 10.1061/(ASCE)WR.1943-5452.0000294.
  • Hassan, W.H., Jassem, M.H., Mohammed, S.S. 2018. A GA-HP model for the optimal design of sewer networks. Water Resources Management, Volume 32, Issue 3, pp 865–879. Doi: 10.1007/s11269-017-1843-y
  • Hiwarkar, A.D., Chauhan, R., Patidar, R., Srivastava, V.C., Singh, S., Mall, I.D. 2021. Binary electrochemical mineralization of heterocyclic nitrogenous compounds: parametric optimization using Taguchi method and mineralization mechanism. Environmental Science and Pollution Research 28:7332–7346. Doi: 10.1007/s11356-020-11057-8
  • Ilgin, M.A., Gupta, S.M. 2010. Comparison of economic benefits of sensor embedded products and conventional products in a multi-product disassembly line. Computers & Industrial Engineering, 59 (4): 748-763. Doi: 10.1016/j.cie.2010.07.031
  • Jalees, M.I. 2020. Synthesis and application of magnetized nanoparticles to remove lead from drinking water: Taguchi design of experiment. Journal of Water, Sanitation and Hygiene for Development, 10.1. Doi: 10.2166/washdev.2020.09
  • Kechagias, J.D., Tsiolikas, A., Petousis, M., Ninikas, K., Vidakis, N., Tzounis, L. 2022. A robust methodology for optimizing the topology and the learning parameters of an ANN for accurate predictions of laser-cut edges surface roughness. Simulation Modelling Practice and Theory 114 102414. Doi: 10.1016/j.simpat.2021.102414
  • Lafifi, B., Rouaiguia, A.,·Boumazza, N. 2019. Optimization of geotechnical parameters using Taguchi’s design of experiment (DOE), RSM and desirability function. Innovative Infrastructure Solutions 4:35. Doi: /10.1007/s41062-019-0218-z
  • Law, A.M. 2007. Simulation modelling and analysis. 4th ed. New York: McGraw Hill.
  • Liang, L.Y., Thompson, R.G., Young, D.M. 2004. Optimising the design of sewer networks using genetic algorithms and tabu search. Engineering, Construction and Architectural Management, Vol. 11 Iss 2 pp. 101 – 112. Doi: 10.1108/09699980410527849
  • Liu, W., Engel, B.A., Chen, W., Wei, W., Wang, Y., Feng, Q. 2021. Quantifying the contributions of structural factors on runoff water quality from green roofs and optimizing assembled combinations using Taguchi method. Journal of Hydrology 593 125864. Doi: 10.1016/j.jhydrol.2020.125864
  • Masoumi, F., Masoumzadeh, S., Zafari, N., Skardi, M.J.E. 2021. Optimum Sanitary Sewer Network Design Using Shuffled Gray Wolf Optimizer. Journal of Pipeline Systems Engineering and Practice 12(4): 04021055. Doi: 10.1061/(ASCE)PS.1949-1204.0000597.
  • Moeini, R. 2019. Ant Intelligent Applied to Sewer Network Design Optimization Problem: Using Four Different Algorithms. Environmental Engineering and Management Journal Vol.18, No.5, 957-971.
  • Moeini, R., and M.H. Afshar, 2017. Arc Based Ant Colony Optimization Algorithm for Optimal Design of Gravitational Sewer Networks. Ain Shams Engineering Journal 8, 207–223. Doi: 10.1016/j.asej.2016.03.003.
  • Moosavi, V., Sadeghi, S.H. 2021. Modeling and optimization of experimental designs for soil loss assessment at plot scale. Journal of Hydrology 592 (2021) 125806. Doi: 10.1016/j.jhydrol.2020.125806
  • Naghedifar, S.M., Ziaei, A.N., Ansari, H. 2020. Numerical analysis and optimization of triggered furrow irrigation system. Irrigation Science 38:287–306. Doi: 10.1007/s00271-020-00672-5
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There are 57 citations in total.

Details

Primary Language English
Subjects Water Resources Engineering, Water Resources and Water Structures
Journal Section Research Articles
Authors

Tülin Çetin 0000-0002-1511-7338

Mehmet Ali Ilgın 0000-0003-1765-2470

Mehmet Ali Yurdusev 0000-0003-3018-0770

Publication Date April 29, 2024
Published in Issue Year 2024 Volume: 14 Issue: 1

Cite

APA Çetin, T., Ilgın, M. A., & Yurdusev, M. A. (2024). Selection of Genetic Algorithm Parameters for Optimization of Storm-Sewer Networks Using Taguchi Method. Karaelmas Fen Ve Mühendislik Dergisi, 14(1), 1-10.
AMA Çetin T, Ilgın MA, Yurdusev MA. Selection of Genetic Algorithm Parameters for Optimization of Storm-Sewer Networks Using Taguchi Method. Karaelmas Fen ve Mühendislik Dergisi. April 2024;14(1):1-10.
Chicago Çetin, Tülin, Mehmet Ali Ilgın, and Mehmet Ali Yurdusev. “Selection of Genetic Algorithm Parameters for Optimization of Storm-Sewer Networks Using Taguchi Method”. Karaelmas Fen Ve Mühendislik Dergisi 14, no. 1 (April 2024): 1-10.
EndNote Çetin T, Ilgın MA, Yurdusev MA (April 1, 2024) Selection of Genetic Algorithm Parameters for Optimization of Storm-Sewer Networks Using Taguchi Method. Karaelmas Fen ve Mühendislik Dergisi 14 1 1–10.
IEEE T. Çetin, M. A. Ilgın, and M. A. Yurdusev, “Selection of Genetic Algorithm Parameters for Optimization of Storm-Sewer Networks Using Taguchi Method”, Karaelmas Fen ve Mühendislik Dergisi, vol. 14, no. 1, pp. 1–10, 2024.
ISNAD Çetin, Tülin et al. “Selection of Genetic Algorithm Parameters for Optimization of Storm-Sewer Networks Using Taguchi Method”. Karaelmas Fen ve Mühendislik Dergisi 14/1 (April 2024), 1-10.
JAMA Çetin T, Ilgın MA, Yurdusev MA. Selection of Genetic Algorithm Parameters for Optimization of Storm-Sewer Networks Using Taguchi Method. Karaelmas Fen ve Mühendislik Dergisi. 2024;14:1–10.
MLA Çetin, Tülin et al. “Selection of Genetic Algorithm Parameters for Optimization of Storm-Sewer Networks Using Taguchi Method”. Karaelmas Fen Ve Mühendislik Dergisi, vol. 14, no. 1, 2024, pp. 1-10.
Vancouver Çetin T, Ilgın MA, Yurdusev MA. Selection of Genetic Algorithm Parameters for Optimization of Storm-Sewer Networks Using Taguchi Method. Karaelmas Fen ve Mühendislik Dergisi. 2024;14(1):1-10.
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