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

Yıl 2024, Cilt: 14 Sayı: 1, 1 - 10, 29.04.2024

Öz

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.

Kaynakça

  • 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
  • Navin, P.K., Mathur, Y.P. 2016. Design Optimization of Sewer System Using Particle Swarm Optimization. Proceedings of Fifth International Conference on Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing 437. Doi: 10.1007/978-981-10-0451-3_17.
  • Pan, T.C., Kao, J.J. 2009. GA-QP model to optimize sewer system design. Journal of Environmental Engineering, Vol. 135, 17-24. Doi: 10.1061/(ASCE)0733-9372(2009)135:1(17)
  • Pham, T.L., Boujelbane, F., Bui, H.N., Nguyen, H.T., Bui, X.-T., Nguyen, D.N., Nguyen, H.T.T., Phan, H.A., Duong, H.T.G., Bui, H.M. 2021. Pesticide production wastewater treatment by electro-fenton using Taguchi experimental design. Water Science & Technology Vol 00 No 0, 1. Doi: 10.2166/wst.2021.372
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Yağmur Suyu-Kanalizasyon Şebekelerinin Optimizasyonunda Taguchi Yöntemi ile Genetik Algoritma Parametrelerinin Seçimi

Yıl 2024, Cilt: 14 Sayı: 1, 1 - 10, 29.04.2024

Öz

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.

Kaynakça

  • 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
  • Navin, P.K., Mathur, Y.P. 2016. Design Optimization of Sewer System Using Particle Swarm Optimization. Proceedings of Fifth International Conference on Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing 437. Doi: 10.1007/978-981-10-0451-3_17.
  • Pan, T.C., Kao, J.J. 2009. GA-QP model to optimize sewer system design. Journal of Environmental Engineering, Vol. 135, 17-24. Doi: 10.1061/(ASCE)0733-9372(2009)135:1(17)
  • Pham, T.L., Boujelbane, F., Bui, H.N., Nguyen, H.T., Bui, X.-T., Nguyen, D.N., Nguyen, H.T.T., Phan, H.A., Duong, H.T.G., Bui, H.M. 2021. Pesticide production wastewater treatment by electro-fenton using Taguchi experimental design. Water Science & Technology Vol 00 No 0, 1. Doi: 10.2166/wst.2021.372
  • Puneeth, H.V., Ganesha Prasad, M.S. 2022. Sustainable in‑situ recycling and IoT‑based monitoring system of water‑soluble metal working fluids. Sustainable Water Resources Management 8:1. Doi: 10.1007/s40899-021-00589-7
  • Rohani, M., Afshar, M.H. 2015. GA–GHCA model for the optimal design of pumped sewer networks. Canadian Journal of Civil Engineering, 42(1): 1-12. Doi: 10.1139/cjce-2014-0187
  • Seifi, A., Ehteram, M., Soroush, F. 2020. Uncertainties of instantaneous influent flow predictions by intelligence models hybridized with multi-objective shark smell optimization algorithm. Journal of Hydrology 587 124977. Doi: 10.1016/j.jhydrol.2020.124977
  • Sharifi, E., Sadjadi, S.J., Aliha, M.R.M., Moniri, A. 2020. Optimization of high-strength self-consolidating concrete mix design using an improved Taguchi optimization method. Construction and Building Materials 236 117547. Doi: 10.1016/j.conbuildmat.2019.117547
  • Siriwardene, N.R., Perera, B.J.C. 2006. Selection of genetic algorithm operators for urban drainage model parameter optimisation. Mathematical and Computer Modelling 44, 415–429. Doi: 10.1016/j.mcm.2006.01.002
  • Taguchi, G. 1986. Introduction to quality engineering: designing quality into products and processes. Tokyo: Asian Productivity Organization.
  • Turan, M.E., Bacak-Turan, G., Cetin, T., Aslan, E. 2019. Feasible Sanitary Sewer Network Generation Using Graph Theory. Advances in Civil Engineering Volume 2019, Article ID 8527180, 15 pages. Doi: 10.1155/2019/8527180.
  • Turton, B. 1994. Optimization of genetic algorithms using the Taguchi method. Journal of Systems Engineering. Volume: 4 Issue: 3 Pages: 121-130.
  • Weng, H.T., Liaw, S.L. 2007. An optimization model for urban sewer system hydraulic design. Journal of the Chinese Institute of Engineers, Vol. 30, No. 1, pp. 31-42. Doi: An optimization model for urban sewer system hydraulic design
  • Weng, H.T., Liaw, S.L., Huang, W.C. 2005. Establishing an optimization model for sewer system layout with applied genetic algorithm. Journal of Environmental Informatics 5 (1) 26-35. Doi: 10.3808/jei.200500043
  • Xia, X., Jiang, S., Nianqing, Z., Li, X., Wang, L. 2019. Genetic algorithm hyper-parameter optimization using Taguchi design for groundwater pollution source identification. IWA Publishing, Water Supply 19 (1): 137-146. Doi: 10.2166/ws.2018.059
  • Yang, W.H., Tarng, Y.S. 1998. Design optimization of cutting parameters for turning operations based on the Taguchi method. Journal of Materials Processing Technology, 84 (1-3): 122-129.Doi: 10.1016/S0924-0136(98)00079-X
  • Zaheri, M.M., Ghanbari, R., Afshar, M.H. 2020. A Two-Phase Simulation–Optimization Cellular Automata Method for Sewer Network Design Optimization. Engineering Optimization 52:4, 620-636. Doi: 10.1080/0305215X.2019.1598983.
  • Zhang, F.B., Wang, Z.L., Yang, M.Y. 2015. Assessing the applicability of the Taguchi design method to an interrill erosion study. Journal of Hydrology 521 65–73. Doi: 10.1016/j.jhydrol.2014.11.059
  • Zhang, F., Wang, M., Yang, M. 2021. Successful application of the Taguchi method to simulated soil erosion experiments at the slope scale under various conditions. Catena 196 (2021) 104835. Doi: 10.1016/j.catena.2020.104835
Toplam 57 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Su Kaynakları Mühendisliği, Su Kaynakları ve Su Yapıları
Bölüm Araştırma Makaleleri
Yazarlar

Tülin Çetin 0000-0002-1511-7338

Mehmet Ali Ilgın 0000-0003-1765-2470

Mehmet Ali Yurdusev 0000-0003-3018-0770

Yayımlanma Tarihi 29 Nisan 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 14 Sayı: 1

Kaynak Göster

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. https://doi.org/10.7212/karaelmasfen.1334274
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. Nisan 2024;14(1):1-10. doi:10.7212/karaelmasfen.1334274
Chicago Çetin, Tülin, Mehmet Ali Ilgın, ve Mehmet Ali Yurdusev. “Selection of Genetic Algorithm Parameters for Optimization of Storm-Sewer Networks Using Taguchi Method”. Karaelmas Fen Ve Mühendislik Dergisi 14, sy. 1 (Nisan 2024): 1-10. https://doi.org/10.7212/karaelmasfen.1334274.
EndNote Çetin T, Ilgın MA, Yurdusev MA (01 Nisan 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, ve M. A. Yurdusev, “Selection of Genetic Algorithm Parameters for Optimization of Storm-Sewer Networks Using Taguchi Method”, Karaelmas Fen ve Mühendislik Dergisi, c. 14, sy. 1, ss. 1–10, 2024, doi: 10.7212/karaelmasfen.1334274.
ISNAD Çetin, Tülin vd. “Selection of Genetic Algorithm Parameters for Optimization of Storm-Sewer Networks Using Taguchi Method”. Karaelmas Fen ve Mühendislik Dergisi 14/1 (Nisan 2024), 1-10. https://doi.org/10.7212/karaelmasfen.1334274.
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 vd. “Selection of Genetic Algorithm Parameters for Optimization of Storm-Sewer Networks Using Taguchi Method”. Karaelmas Fen Ve Mühendislik Dergisi, c. 14, sy. 1, 2024, ss. 1-10, doi:10.7212/karaelmasfen.1334274.
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.