Araştırma Makalesi
BibTex RIS Kaynak Göster

A modified grasshopper optimization algorithm combined with wavelet functions

Yıl 2024, Cilt: 8 Sayı: 2, 1 - 15, 28.12.2024

Öz

The present study analyzes the social interaction function of grasshoppers using five alternative wavelet functions for the original function of grasshoppers. In this research, the Morlet, Polywog1, Polywog3, Rasp1, and Rasp3 wavelet functions have been selected as possible substitutes for the wavelet function. The first structure is a three-member truss, it is optimized under the constraints of tension, deformation, and buckling, to reduce the weight of the truss. The second structure is a cantilever beam, with five hollow square beam sections, with the target function aiming to minimize the total weight of the beam. This research aims to present a proposed model combining the grasshopper algorithm and wavelet functions to improve the convergence speed and results of the grasshopper algorithm. The research results show that replacing the wavelet functions does not change much in the weight of the first benchmark structure, but it provides acceptable accuracy. The Polywog1 algorithm demonstrates superior performance, converging faster than GOA, with a marginal difference of 2.64×10^-8 percent in weight. In addition, the Rasp3 algorithm shows the best result with 6.46×10-10 percent more weight than GOA. In the cantilever beam structure, the optimization has been improved and, in all cases, the convergence speed has been evaluated as appropriate. Moreover, only Morlet wavelet functions have provided a suitable solution while other wavelet functions have not been successful in this field. Adding wavelet functions as the interaction function of the grasshoppers removes the source of error, which includes the l and f parameters, in the new possible functions.

Etik Beyan

-

Destekleyen Kurum

-

Proje Numarası

-

Teşekkür

-

Kaynakça

  • [1] Coello C.A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput. Meth Appl Mech Eng. 191(11-12); 1245-1287.
  • [2] Marler R.T., Arora J.S. (2004) Survey of multi-objective optimization methods for engineering. Struct Multidiscipl Optim. 26; 369–395.
  • [3] Saremi S., Mirjalali S., Lewis A. (2017). Grasshopper Optimisation Algorithm: Theory and application. Advances in Engineering Software. 105; 30-47.
  • [4] Mehraihi Y., Gabis A.B., Mirjalali S.A., Cherif A.R. (2021). Grasshopper Optimization Algorithm: Theory, Variants, and Applications, In IEEE Access, 9; 50001-50024.
  • [5] Slowik A. (2021). Swarm Intelligence Algorithms: A Tutorial. CRC Press-Taylor & Francis Group.
  • [6] Lukasik S., Kowalski P.A., Charytanowicz M., Kulczycki P. (2017). Data clustering with grasshopper optimization algorithm. Federated Conference on Computer Science and Information Systems (FedCSIS) 71-74.
  • [7] Feng H., Ni H., Zhao R., Zhu X. (2020). An Enhanced Grasshopper Optimization Algorithm to the Bin Packing Problem. Journal of Control Science and Engineering, 2020; 19.
  • [8] Hichem H., Elkamel M., Rafik M., Mesaaoud M.T., Ouahiba C. (2022) A new binary grasshopper optimization algorithm for feature selection problem. J. King Saud Univ.-Comput. Inf. Sci. 34(2); 316-328.
  • [9] Saxena A., Shekhawat S., Kumar R. Application and development of enhanced chaotic grasshopper optimization algorithms. Model. Simul. Eng. 2018; 1-14.
  • [10] Yue X., Zhang H., Yu H. (2020). A Hybrid Grasshopper Optimization Algorithm with Invasive Weed for Global Optimization. IEEE Access. 8; 5928-5960. Doi: 10.1109/ACCESS.2019.2963679
  • [11] Sulaiman, M. (2019). Implementation of improved grasshopper optimization algorithm to solve economic load dispatch problems. Hacettepe Journal of Mathematics and Statistics, 1–21. Doi:10.15672/hujms.507579
  • [12] Luo, J., Chen, H., Zhang, Q., Xu, Y., Huang, H., Zhao, X. (2018). An improved grasshopper optimization algorithm with application to financial stress prediction. Applied Mathematical Modelling, 64, 654–668. Doi: 10.1016/j.apm.2018.07.044
  • [13] Taher M.A., Kamel S., Jurado F., Ebeed M. (2018). Modified grasshopper optimization framework for optimal power flow solution, Electrical Engineering. 101; 121–148.
  • [14] Zhou H., Ding Z., Peng H., Tang Z., Liang G., Chen H., Ma C., Wang, M. (2020). An Improved Grasshopper Optimizer for Global Tasks. Complexity, 2020; 1-23. Doi:10.1155/2020/4873501.
  • [15] Goel N., Grover B., Anuj, Gupta D., Khanna A., Sharma M. (2020). Modified Grasshopper Optimization Algorithm for detection of Autism Spectrum Disorder. Physical Communication 41; 101-115.
  • [16] Seifollahi, M., Lotfollahi-Yaghin, A., Kalateh, F., Daneshfaraz, R., Abbasi, S., Abraham, J. (2021). Estimation of the Local Scour from a Cylindrical Bridge Pier Using a Compilation Wavelet Model and Artificial Neural Network. Journal of Hydraulic Structures, 7; 1-22. doi: 10.22055/jhs.2021.38300.1187
  • [17] Seifollahi M., Abbasi S., Abraham J., Norouzi R., Daneshfaraz R., Lotfollahi-Yaghin M.A., Alkan A. (2022). Optimization of Gravity Concrete Dams Using the Grasshopper Algorithm (Case Study: Koyna Dam), Geotech Geol. Eng., 40; 5481-5496.
  • [18] Seifollahi, M., Abbasi, S., Pourtaghi, A., Daneshfaraz, R., Abraham, J., Parvaresh, M., Alkan, A. (2022). Performance efficiency of data-based hybrid intelligent approaches to predict crest settlement in rockfill dams. Arab J Geosci 15; 1701. https://doi.org/10.1007/s12517-022-11005-5
  • [19] Mirjalili S.Z., Mirjalili S., Saremi S., Faris H., Aljarah I. (2017). Grasshopper optimization algorithm for multi-objective optimization problems, Appl. Intell., 48; 805-820.
  • [20] Utama D.M., Baroto T., Setiya Widodo D. (2020). Energy-efficient flow shop scheduling using hybrid Grasshopper algorithm optimization. J Ilmiah Teknik Ind 19(1);30-38. https:// doi. org/ 10. 23917/ jiti. v19i1. 10079.
  • [21] Abbasi, S., Seifollahi, M., Farzaneh, S., Daneshfaraz, R., Süme, V., Sadraei, N., & Abraham, J. (2024). Design optimization of concrete gravity dams using grasshopper optimization algorithm. Innovative Infrastructure Solutions, 9(12), 453.
  • [22] Algamal Z.Y., Qasim M.K., Lee M.H., Ali H.T.M. (2021). Improving grasshopper optimization algorithm for hyperparameters estimation and feature selection in support vector regression, Chemometrics and Intelligent Laboratory Systems, 208.
  • [23] Abbaszadeh, H., Norouzi, R., Sume, V., Kuriqi, A., Daneshfaraz, R., & Abraham, J. (2023). Sill role effect on the flow characteristics (experimental and regression model analytical). Fluids, 8(8), 235.
  • [24] Abbasi, S., Seifollahi, M., Daneshfaraz, R. Mohammadi, F., Abraham, J., Abbaszadeh, H. (2023). Estimation of Vertical Settlement of Earthen Dams Caused by Earthquake Using ANN Model and Wavelet-ANN Composition. Geotech Geol Eng 41; 3169–3186. https://doi.org/10.1007/s10706-023-02451-3 ‏‏ [25] Daneshfaraz, R., Norouzi, R., Abbaszadeh, H., & Azamathulla, H. M. (2022). Theoretical and experimental analysis of applicability of sill with different widths on the gate discharge coefficients. Water Supply, 22(10), 7767-7781.
  • [26] Qin P., Hu H., Yang Z. (2021). The improved grasshopper optimization algorithm and its applications. Sci Rep, 11; 23733. https://doi.org/10.1038/s41598-021-03049-6
  • [27] Süme, V., Daneshfaraz, R., Kerim, A., Abbaszadeh, H., & Abraham, J. (2024). Investigation of clean energy production in drinking water networks. Water Resources Management, 38(6), 2189-2208.
  • [28] Abbasi, S., Seifollahi, M., Farnian, M., Mohammadi, F. (2024). Optimizing the Geometric Dimensions of Feriant Dam Using the Grasshopper Algorithm, The 22nd Iranian Hydraulic Conference, 22 April.
  • [29] Abbaszadeh, H., Daneshfaraz, R., Sume, V., & Abraham, J. (2024). Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions. AQUA—Water Infrastructure, Ecosystems and Society, 73(3), 637-661.
  • [30] Daneshfaraz, R., Norouzi, R., Ebadzadeh, P. (2022). Experimental and numerical study of sluice gate flow pattern with non- suppressed sill and its effect on discharge coefficient in free-flow conditions, Journal of Hydraulic Structures, 8(1); 1-20. doi: 10.22055/jhs.2022.40089.1201
  • [31] Topaz C.M., Bernoff A.J., Logan S., Toolson W. (2008). A model for rolling swarms of locusts. Eur Phys J Special Top.157; 93–109.
  • [32] Mirjalili S. (2015). The ant lion optimizer, Advances in Engineering Software 83; 80-98.
  • [33] Zhang M., Luo W., Wang X. (2008). Differential evolution with dynamic stochastic se- lection for constrained optimization. Inf Sci, 178;3043–74.
  • [34] Liu H., Cai Z., Wang Y. (2010). Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10; 629–40.
  • [35] Sadollah A., Bahreininejad A., Eskandar H., Hamdi M. (2013). Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput,13; 2592–612.
  • [36] Ray T., Saini P. (2001). Engineering design optimization using a swarm with an intelli- gent information sharing among individuals. Eng Optim, 33; 735–48.
  • [37] Tsai J-F. (2005). Global optimization of nonlinear fractional programming problems in engineering design. Eng Optim. 37:399–409.
  • [38] Gandomi A.H., Yang X-S., Alavi A.H. (2013). Cuckoo search algorithm: a metaheuristic ap- proach to solve structural optimization problems. Eng Comput. 29; 17–35.
  • [39] Svanberg K. (1987), The Method of Moving Asymptotes-A New Method for Structural Optimization, International Journal for Numerical Methods in Engineering, 24; 359-373.
  • [40] Chickermane H, Gea HC. (1996) Structural optimization using a new local approximation method, Int J Numer Meth Eng. 39; 829-46.
  • [41] Parrilo P.A., & Thomas R.R. (2020). Sum of squares: theory and applications: Ams short course sum of squares: theory and applications January, 14-15, 2019 baltimore maryland. American Mathematical Society.

A modified grasshopper optimization algorithm combined with wavelet functions

Yıl 2024, Cilt: 8 Sayı: 2, 1 - 15, 28.12.2024

Öz

The present study analyzes the social interaction function of grasshoppers using five alternative wavelet functions for the original function of grasshoppers. In this research, the Morlet, Polywog1, Polywog3, Rasp1, and Rasp3 wavelet functions have been selected as possible substitutes for the wavelet function. The first structure is a three-member truss, it is optimized under the constraints of tension, deformation, and buckling, to reduce the weight of the truss. The second structure is a cantilever beam, with five hollow square beam sections, with the target function aiming to minimize the total weight of the beam. This research aims to present a proposed model combining the grasshopper algorithm and wavelet functions to improve the convergence speed and results of the grasshopper algorithm. The research results show that replacing the wavelet functions does not change much in the weight of the first benchmark structure, but it provides acceptable accuracy. The Polywog1 algorithm demonstrates superior performance, converging faster than GOA, with a marginal difference of 2.64×10^-8 percent in weight. In addition, the Rasp3 algorithm shows the best result with 6.46×10-10 percent more weight than GOA. In the cantilever beam structure, the optimization has been improved and, in all cases, the convergence speed has been evaluated as appropriate. Moreover, only Morlet wavelet functions have provided a suitable solution while other wavelet functions have not been successful in this field. Adding wavelet functions as the interaction function of the grasshoppers removes the source of error, which includes the l and f parameters, in the new possible functions.

Etik Beyan

-

Destekleyen Kurum

-

Proje Numarası

-

Teşekkür

-

Kaynakça

  • [1] Coello C.A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput. Meth Appl Mech Eng. 191(11-12); 1245-1287.
  • [2] Marler R.T., Arora J.S. (2004) Survey of multi-objective optimization methods for engineering. Struct Multidiscipl Optim. 26; 369–395.
  • [3] Saremi S., Mirjalali S., Lewis A. (2017). Grasshopper Optimisation Algorithm: Theory and application. Advances in Engineering Software. 105; 30-47.
  • [4] Mehraihi Y., Gabis A.B., Mirjalali S.A., Cherif A.R. (2021). Grasshopper Optimization Algorithm: Theory, Variants, and Applications, In IEEE Access, 9; 50001-50024.
  • [5] Slowik A. (2021). Swarm Intelligence Algorithms: A Tutorial. CRC Press-Taylor & Francis Group.
  • [6] Lukasik S., Kowalski P.A., Charytanowicz M., Kulczycki P. (2017). Data clustering with grasshopper optimization algorithm. Federated Conference on Computer Science and Information Systems (FedCSIS) 71-74.
  • [7] Feng H., Ni H., Zhao R., Zhu X. (2020). An Enhanced Grasshopper Optimization Algorithm to the Bin Packing Problem. Journal of Control Science and Engineering, 2020; 19.
  • [8] Hichem H., Elkamel M., Rafik M., Mesaaoud M.T., Ouahiba C. (2022) A new binary grasshopper optimization algorithm for feature selection problem. J. King Saud Univ.-Comput. Inf. Sci. 34(2); 316-328.
  • [9] Saxena A., Shekhawat S., Kumar R. Application and development of enhanced chaotic grasshopper optimization algorithms. Model. Simul. Eng. 2018; 1-14.
  • [10] Yue X., Zhang H., Yu H. (2020). A Hybrid Grasshopper Optimization Algorithm with Invasive Weed for Global Optimization. IEEE Access. 8; 5928-5960. Doi: 10.1109/ACCESS.2019.2963679
  • [11] Sulaiman, M. (2019). Implementation of improved grasshopper optimization algorithm to solve economic load dispatch problems. Hacettepe Journal of Mathematics and Statistics, 1–21. Doi:10.15672/hujms.507579
  • [12] Luo, J., Chen, H., Zhang, Q., Xu, Y., Huang, H., Zhao, X. (2018). An improved grasshopper optimization algorithm with application to financial stress prediction. Applied Mathematical Modelling, 64, 654–668. Doi: 10.1016/j.apm.2018.07.044
  • [13] Taher M.A., Kamel S., Jurado F., Ebeed M. (2018). Modified grasshopper optimization framework for optimal power flow solution, Electrical Engineering. 101; 121–148.
  • [14] Zhou H., Ding Z., Peng H., Tang Z., Liang G., Chen H., Ma C., Wang, M. (2020). An Improved Grasshopper Optimizer for Global Tasks. Complexity, 2020; 1-23. Doi:10.1155/2020/4873501.
  • [15] Goel N., Grover B., Anuj, Gupta D., Khanna A., Sharma M. (2020). Modified Grasshopper Optimization Algorithm for detection of Autism Spectrum Disorder. Physical Communication 41; 101-115.
  • [16] Seifollahi, M., Lotfollahi-Yaghin, A., Kalateh, F., Daneshfaraz, R., Abbasi, S., Abraham, J. (2021). Estimation of the Local Scour from a Cylindrical Bridge Pier Using a Compilation Wavelet Model and Artificial Neural Network. Journal of Hydraulic Structures, 7; 1-22. doi: 10.22055/jhs.2021.38300.1187
  • [17] Seifollahi M., Abbasi S., Abraham J., Norouzi R., Daneshfaraz R., Lotfollahi-Yaghin M.A., Alkan A. (2022). Optimization of Gravity Concrete Dams Using the Grasshopper Algorithm (Case Study: Koyna Dam), Geotech Geol. Eng., 40; 5481-5496.
  • [18] Seifollahi, M., Abbasi, S., Pourtaghi, A., Daneshfaraz, R., Abraham, J., Parvaresh, M., Alkan, A. (2022). Performance efficiency of data-based hybrid intelligent approaches to predict crest settlement in rockfill dams. Arab J Geosci 15; 1701. https://doi.org/10.1007/s12517-022-11005-5
  • [19] Mirjalili S.Z., Mirjalili S., Saremi S., Faris H., Aljarah I. (2017). Grasshopper optimization algorithm for multi-objective optimization problems, Appl. Intell., 48; 805-820.
  • [20] Utama D.M., Baroto T., Setiya Widodo D. (2020). Energy-efficient flow shop scheduling using hybrid Grasshopper algorithm optimization. J Ilmiah Teknik Ind 19(1);30-38. https:// doi. org/ 10. 23917/ jiti. v19i1. 10079.
  • [21] Abbasi, S., Seifollahi, M., Farzaneh, S., Daneshfaraz, R., Süme, V., Sadraei, N., & Abraham, J. (2024). Design optimization of concrete gravity dams using grasshopper optimization algorithm. Innovative Infrastructure Solutions, 9(12), 453.
  • [22] Algamal Z.Y., Qasim M.K., Lee M.H., Ali H.T.M. (2021). Improving grasshopper optimization algorithm for hyperparameters estimation and feature selection in support vector regression, Chemometrics and Intelligent Laboratory Systems, 208.
  • [23] Abbaszadeh, H., Norouzi, R., Sume, V., Kuriqi, A., Daneshfaraz, R., & Abraham, J. (2023). Sill role effect on the flow characteristics (experimental and regression model analytical). Fluids, 8(8), 235.
  • [24] Abbasi, S., Seifollahi, M., Daneshfaraz, R. Mohammadi, F., Abraham, J., Abbaszadeh, H. (2023). Estimation of Vertical Settlement of Earthen Dams Caused by Earthquake Using ANN Model and Wavelet-ANN Composition. Geotech Geol Eng 41; 3169–3186. https://doi.org/10.1007/s10706-023-02451-3 ‏‏ [25] Daneshfaraz, R., Norouzi, R., Abbaszadeh, H., & Azamathulla, H. M. (2022). Theoretical and experimental analysis of applicability of sill with different widths on the gate discharge coefficients. Water Supply, 22(10), 7767-7781.
  • [26] Qin P., Hu H., Yang Z. (2021). The improved grasshopper optimization algorithm and its applications. Sci Rep, 11; 23733. https://doi.org/10.1038/s41598-021-03049-6
  • [27] Süme, V., Daneshfaraz, R., Kerim, A., Abbaszadeh, H., & Abraham, J. (2024). Investigation of clean energy production in drinking water networks. Water Resources Management, 38(6), 2189-2208.
  • [28] Abbasi, S., Seifollahi, M., Farnian, M., Mohammadi, F. (2024). Optimizing the Geometric Dimensions of Feriant Dam Using the Grasshopper Algorithm, The 22nd Iranian Hydraulic Conference, 22 April.
  • [29] Abbaszadeh, H., Daneshfaraz, R., Sume, V., & Abraham, J. (2024). Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions. AQUA—Water Infrastructure, Ecosystems and Society, 73(3), 637-661.
  • [30] Daneshfaraz, R., Norouzi, R., Ebadzadeh, P. (2022). Experimental and numerical study of sluice gate flow pattern with non- suppressed sill and its effect on discharge coefficient in free-flow conditions, Journal of Hydraulic Structures, 8(1); 1-20. doi: 10.22055/jhs.2022.40089.1201
  • [31] Topaz C.M., Bernoff A.J., Logan S., Toolson W. (2008). A model for rolling swarms of locusts. Eur Phys J Special Top.157; 93–109.
  • [32] Mirjalili S. (2015). The ant lion optimizer, Advances in Engineering Software 83; 80-98.
  • [33] Zhang M., Luo W., Wang X. (2008). Differential evolution with dynamic stochastic se- lection for constrained optimization. Inf Sci, 178;3043–74.
  • [34] Liu H., Cai Z., Wang Y. (2010). Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10; 629–40.
  • [35] Sadollah A., Bahreininejad A., Eskandar H., Hamdi M. (2013). Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput,13; 2592–612.
  • [36] Ray T., Saini P. (2001). Engineering design optimization using a swarm with an intelli- gent information sharing among individuals. Eng Optim, 33; 735–48.
  • [37] Tsai J-F. (2005). Global optimization of nonlinear fractional programming problems in engineering design. Eng Optim. 37:399–409.
  • [38] Gandomi A.H., Yang X-S., Alavi A.H. (2013). Cuckoo search algorithm: a metaheuristic ap- proach to solve structural optimization problems. Eng Comput. 29; 17–35.
  • [39] Svanberg K. (1987), The Method of Moving Asymptotes-A New Method for Structural Optimization, International Journal for Numerical Methods in Engineering, 24; 359-373.
  • [40] Chickermane H, Gea HC. (1996) Structural optimization using a new local approximation method, Int J Numer Meth Eng. 39; 829-46.
  • [41] Parrilo P.A., & Thomas R.R. (2020). Sum of squares: theory and applications: Ams short course sum of squares: theory and applications January, 14-15, 2019 baltimore maryland. American Mathematical Society.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İnşaat Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Salim Abbasi

Mehran Seifollahi

Firouz Mohammadi

Saeed Khedmati

Milad Kheiry

Proje Numarası -
Yayımlanma Tarihi 28 Aralık 2024
Gönderilme Tarihi 4 Kasım 2024
Kabul Tarihi 21 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 8 Sayı: 2

Kaynak Göster

APA Abbasi, S., Seifollahi, M., Mohammadi, F., Khedmati, S., vd. (2024). A modified grasshopper optimization algorithm combined with wavelet functions. Türk Hidrolik Dergisi, 8(2), 1-15.
  • "Türk Hidrolik Dergisi"nin Tarandığı INDEX'ler 
  • (Indexes : Turkish Journal of Hydraulic)       

   18820


18821

 
18985              18822                  18823                                     

  

       18824