Relevant bibliographies by topics / Grasshopper Optimization Algorithm(GOA)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Grasshopper Optimization Algorithm(GOA)'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Grasshopper Optimization Algorithm(GOA)"

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Steczek, Marcin, Włodzimierz Jefimowski, and Adam Szeląg. "Application of Grasshopper Optimization Algorithm for Selective Harmonics Elimination in Low-Frequency Voltage Source Inverter." Energies 13, no. 23 (2020): 6426. http://dx.doi.org/10.3390/en13236426.

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In this paper, an application of the recently developed Grasshopper Optimization Algorithm (GOA) for calculation of switching angles for Selective Harmonic Elimination (SHE) PWM in low-frequency voltage source inverter is proposed. The algorithm is based on insect behavior in the food foraging swarm of grasshoppers. The characteristic feature of GOA is the movement of agents is based on the position of all agents in the swarm. This method represents a higher probability of convergence than Particle Swarm Optimization (PSO) Modifications of GOA have been examined regarding their effect on the algorithm’s convergence. The proposed modifications were based on the following techniques: Grey Wolf Optimizer (GWO), Natural Selection (NS), Adaptive Grasshopper Optimization Algorithm (AGOA), and Opposite Based Learning (OBL). The performance of GOA and its modifications were compared with well-known PSO. Areas, where GOA is superior to PSO in terms of probability of convergence, have been shown. The efficiency of the GOA algorithm applied for solving the SHE problem was confirmed by measurements in the laboratory.
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Naomi, Anatasia, Asri Bekti Pratiwi, and Herry Suprajitno. "Grasshopper Optimizaton Algorithm (GOA) untuk Menyelesaikan Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD)." Tensor: Pure and Applied Mathematics Journal 3, no. 2 (2022): 73–84. http://dx.doi.org/10.30598/tensorvol3iss2pp73-84.

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The purpose of this paper is to solve the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) using the Grasshopper Optimization Algorithm (GOA). Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is a problem of forming routes that serve each customer, by delivering and retrieving simultaneously. The purpose of VRPSPD is to minimize the total mileage to serve all customers with the limit that each customer is served exactly once, and the vehicle load does not exceed its maximum capacity. Grasshopper Optimization Algorithm (GOA) is an algorithm inspired by nature by mimicking the living behavior of grasshopper swarms in search of food sources. GOA has several main stages, namely initialization of parameters, determination of target grasshoppers, calculating the coefficient of decline, calculating the distance between grasshoppers, and calculating the new position of the grasshoppers. Implementation of the GOA program to complete VRPSPD using the C++ programming language using 3 types of data, data with 13 customers, 22 customers, and 100 customers. Based on the results of the running program, it can be concluded that the more iterations and the number of populations, the solution obtained tends to be better.
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Y. Abdalla, Abdulkareem, and Turki Y. Abdalla. "A new modified grasshopper optimization algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 5 (2022): 2756–63. http://dx.doi.org/10.11591/eei.v11i5.4083.

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The grasshopper algorithm (GOA) is a recent algorithm. It is widely used in many applications and results in a good solution. The algorithm is simple and the accuracy in very high. The GOA has some limitations due to the use of linear comfort zone parameter that causes some difficulties in balancing between the exploration and exploitation which may lead to fall in a local optimum. In this paper a modification is made to improve the operation of GOA. A nonlinear function is developed to replace the linear comfort zone parameter. The benchmark of GOA authors is used for testing the performance improvement of the suggested modified GOA compared to the basic GOA. Results indicate that the MGOA outperforms original GOA, presenting a higher accuracy, faster convergence, and stronger stability. The proposed new modified GOA performs better than the original GOA.
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Osman, Hanaa Mohammed, Rahma Saleem Alsawaf, and Asma'a Yaseen Hammo. "Survey of using grasshopper algorithm." Technium: Romanian Journal of Applied Sciences and Technology 4, no. 3 (2022): 37–44. http://dx.doi.org/10.47577/technium.v4i3.6344.

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The metaheuristic optimization algorithm is used to explain a large region solution space. One of these algorithms is a grasshopper which divides the search process into exploitation and exploration. This article focuses on research efforts directed at gaining a clear understanding of the behavior of grasshoppers and it is using optimization algorithms. It is concluded that the benefits have been effective in answering global unrestricted and restricted optimization issues, easy development, high accuracy, and obtaining a good solution. However, the disadvantages of the GOA algorithm are simple to fall into local optimum and slow convergence speed.
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Alsammarraie, Samer, and Nazar K. Hussein. "A New Hybrid Grasshopper Optimization - Backpropagation for Feedforward Neural Network Training." Tikrit Journal of Pure Science 25, no. 1 (2020): 118. http://dx.doi.org/10.25130/j.v25i1.944.

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The Grasshopper optimization algorithm showed a rapid converge in the initial phases of the global search, however while being around the global optimum, the searching process became so slow. On the contrary, the gradient descending method around achieved faster convergent speed global optimum, and the convergent accuracy was showed to be higher at the same time. As a result, the proposed hybrid algorithm combined Grasshopper optimization algorithm (GOA) along with the back-propagation (BP) algorithm, also referred to as GOA–BP algorithm, was introduced to provide training to the weights of the feed forward neural network (FNN), the proposed hybrid algorithm can utilize the strong global searching ability of the GOA, and the intense local searching ability of the Back-Propagation algorithm. The results of experiments showed that the proposed hybrid GOA–BP algorithm was better and faster in convergent speed and accuracy than the Grasshopper optimization algorithm (GOA) and BP algorithm.
 
 http://dx.doi.org/10.25130/tjps.25.2020.018
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Samer Alsammarraie and Nazar K. Hussein. "A New Hybrid Grasshopper Optimization - Backpropagation for Feedforward Neural Network Training." Tikrit Journal of Pure Science 25, no. 1 (2023): 118–27. http://dx.doi.org/10.25130/tjps.v25i1.221.

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The Grasshopper optimization algorithm showed a rapid converge in the initial phases of the global search, however while being around the global optimum, the searching process became so slow. On the contrary, the gradient descending method around achieved faster convergent speed global optimum, and the convergent accuracy was showed to be higher at the same time. As a result, the proposed hybrid algorithm combined Grasshopper optimization algorithm (GOA) along with the back-propagation (BP) algorithm, also referred to as GOA–BP algorithm, was introduced to provide training to the weights of the feed forward neural network (FNN), the proposed hybrid algorithm can utilize the strong global searching ability of the GOA, and the intense local searching ability of the Back-Propagation algorithm. The results of experiments showed that the proposed hybrid GOA–BP algorithm was better and faster in convergent speed and accuracy than the Grasshopper optimization algorithm (GOA) and BP algorithm.
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Wang, Hengfeng, Chao Liu, Huaning Wu, Bin Li, and Xu Xie. "Optimal Pattern Synthesis of Linear Array and Broadband Design of Whip Antenna Using Grasshopper Optimization Algorithm." International Journal of Antennas and Propagation 2020 (January 20, 2020): 1–14. http://dx.doi.org/10.1155/2020/5904018.

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Antenna arrays with high directivity, low side-lobe level, and null control in desired direction and whip antenna with wider bandwidth both need to be optimized to meet different needs of communication systems. A new natural heuristic algorithm simulating social behavior of grasshoppers, grasshopper optimization algorithm (GOA), is applied to electromagnetic field as a new effective technology to solve the antenna optimization problem for the first time. Its algorithm is simple and has no gradient mechanism, can effectively avoid falling into local optimum, and is suitable for single-objective and multiobjective optimization problems. GOA is used to optimize the side lobe suppression, null depth, and notch control of arbitrary linear array and then used to optimize the loading and matching network of 10-meter HF broadband whip antenna compared with other algorithms. The results show that GOA has more advantages in side-lobe suppression, null depth, and notch control of linear array than other algorithms and has better broadband optimization performance for HF whip antenna. The pattern synthesis and antenna broadband optimization based on GOA provide a new and effective method for antenna performance optimization.
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Osman-Ali, Najwan, and Junita Mohamad-Saleh. "An Adaptive Average Grasshopper Optimization Algorithm for Solving Numerical Optimization Problems." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 18 (May 10, 2023): 121–35. http://dx.doi.org/10.37394/23203.2023.18.13.

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The grasshopper optimization algorithm (GOA), inspired by the behavior of grasshopper swarms, has proven efficient in solving globally constrained optimization problems. However, the original GOA exhibits some shortcomings in that its original linear convergence parameter causes the exploration and exploitation processes to be unbalanced, leading to a slow convergence speed and a tendency to fall into a local optimum trap. This study proposes an adaptive average GOA (AAGOA) with a nonlinear convergence parameter that can improve optimization performance by overcoming the shortcomings of the original GOA. To evaluate the optimization capability of the proposed AAGOA, the algorithm was tested on the CEC2021 benchmark set, and its performance was compared to that of the original GOA. According to the analysis of the results, AAGOA is ranked first in the Friedman ranking test and can produce better optimization results compared to its counterparts.
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Zhou, Hanfeng, Zewei Ding, Hongxin Peng, et al. "An Improved Grasshopper Optimizer for Global Tasks." Complexity 2020 (September 23, 2020): 1–23. http://dx.doi.org/10.1155/2020/4873501.

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The grasshopper optimization algorithm (GOA) is a metaheuristic algorithm that mathematically models and simulates the behavior of the grasshopper swarm. Based on its flexible, adaptive search system, the innovative algorithm has an excellent potential to resolve optimization problems. This paper introduces an enhanced GOA, which overcomes the deficiencies in convergence speed and precision of the initial GOA. The improved algorithm is named MOLGOA, which combines various optimization strategies. Firstly, a probabilistic mutation mechanism is introduced into the basic GOA, which makes full use of the strong searchability of Cauchy mutation and the diversity of genetic mutation. Then, the effective factors of grasshopper swarm are strengthened by an orthogonal learning mechanism to improve the convergence speed of the algorithm. Moreover, the application of probability in this paper greatly balances the advantages of each strategy and improves the comprehensive ability of the original GOA. Note that several representative benchmark functions are used to evaluate and validate the proposed MOLGOA. Experimental results demonstrate the superiority of MOLGOA over other well-known methods both on the unconstrained problems and constrained engineering design problems.
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Sharma, Satender, Usha Chauhan, Ruqaiya Khanam, and Krishna Kant Singh. "Digital Watermarking using Grasshopper Optimization Algorithm." Open Computer Science 11, no. 1 (2021): 330–36. http://dx.doi.org/10.1515/comp-2019-0023.

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Abstract The advancement in computer science technology has led to some serious concerns about the piracy and copyright of digital content. Digital watermarking technique is widely used for copyright protection and other similar applications. In this paper, a technique for digital watermarking based on Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Grasshopper Optimization Algorithm (GOA) is proposed. The method computes the DWT of the cover image to obtain the sub-components of the image. The subcomponent is converted to frequency domain using DCT. The challenge is to find the optimal scaling factor to be used for watermarking. The authors have designed a GOA based technique that finds the optimized scaling factor and the coefficient for embedding the watermark. GOA makes the watermark undetectable and is invisible in the cover image. The watermark image is embedded in the cover image using these coefficients. The extraction of watermark from the cover image is done by using inverse DCT and DWT. The proposed method is compared with the other state of the art methods. The effectiveness of the proposed method is computed using Peak Signal to Noise Ratio (PSNR), Normalized Cross Correlation (NCC) and Image Fidelity (IF). The proposed method outperforms the other methods and can be effectively used for practical digital watermarking.
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Dissertations / Theses on the topic "Grasshopper Optimization Algorithm(GOA)"

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Granberg, Andreas, and Joel Wahlstein. "Parametric design and optimization of pipe bridges : Automating the design process in early stage of design." Thesis, KTH, Bro- och stålbyggnad, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277935.

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Parametric design can be used for structural design. This approach has someclear advantages compared to the conventional point-based approach using differentComputer Aided Design (CAD)-software, especially in early stage of design. Since themodel is parametrically defined, alternate designs, that are within the scope of theparametric definition, can be explored with little effort from the user compared tothe point-based models. In this way, optimization routines can be used to make moreinformed decisions about the design. Pipe bridges usually have a similar design that issuitable to be defined parametrically.The aim of the thesis is to automate the modeling of pipe bridges in the earlystages of design, to make an integrated analysis and to optimize the structure withregard to material cost and carbon dioxide equivalent-emissions as well as mass ofthe structure. Further, to investigate in what way these objectives are correlated.This thesis improves an existing grasshopper script used to design pipe bridges andimplement an automatic generation of a Bill of Quantity (BoQ).The results of the thesis case study suggests that there is potential in usingoptimization with parametric design to minimize the cost of pipe bridges. With a goodparametric design definition alternate designs can be explored with little effort fromthe user. This benefit to speed up the design process, and allowing the designer towork with adaptable design, could be reasons to turn to a parametric design method.It should also be stressed that this thesis suggests a correlation between the cost of thestructure and the carbon dioxide equivalent emission from the structure. Meaning thatwhile minimizing emissions one could also be minimizing the cost.
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Bozhinovski, Konstantin. "Generative design of a nature-inspired geometry manipulated by an algorithm in a BIM-environment, applied in a façade system for a residential building in Bologna, Italy." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21501/.

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In terms of technology, BIM is also part of the worldwide change Industry 4.0, which in essence is the trend toward automation and data exchange in manufacturing technologies and processes. Generative design is an iterative process that involves a program that will generate a certain number of outputs that meet certain constraints, so that a designer is able to fine tune the feasible project by changing minimal and maximal values of an interval in which a variable of the program meets the set of constraints, in order to reduce or augment the number of outputs to choose from. The initial idea of this thesis work was to manipulate few of the most basic geometric elements in order to get a complex parametric shape inspired from the honeycomb as the natures perfectly generated the element. This preliminary idea, together with the ambition to use this transformation for a façade system in a structural building led us to a series of decisions to try and connect two “worlds”, in the sense that we have a CAD environment that lets us create the geometry and a BIM environment where everything is represented by a specific level of information. This geometry is given a specific set of rules that drive and manipulate each of the elements it contains in a certain fashion. This methodology, as well as the communication and the interaction between the software adopted and their programming environments, is what makes the generative design possible. This result from the Grasshopper algorithm is then being created in the CAD environment in Rhinoceros3D, which then can be opened through Rhino.Inside.Revit and give us a direct real-time preview in the BIM environment in Revit. Through a long series of testing and experimenting with the geometry, we get to a point where we have a functional algorithm that creates and manipulates the geometry, in order to foster many design opportunities for structural and architectural designers.
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Book chapters on the topic "Grasshopper Optimization Algorithm(GOA)"

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Okwu, Modestus O., and Lagouge K. Tartibu. "Grasshopper Optimisation Algorithm (GOA)." In Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61111-8_10.

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Harandi, Negin, Arnout Van Messem, Wesley De Neve, and Joris Vankerschaver. "Grasshopper Optimization Algorithm (GOA): A Novel Algorithm or A Variant of PSO?" In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70932-6_7.

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Zhu, Yushan, Zhu Yang, and Jian Huang. "Intelligent Prediction on Cement Take of Dam Foundation Grouting Based on GOA-ELM Model." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-7251-3_11.

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AbstractAccurate and reasonable cement take prediction is of great significance for effective control of dam foundation grouting quality and cost. This article combined the previous research results and engineering practice to explore the different influencing factors of cement take, and conducted parameter correlation analysis to determine the input parameters for prediction. Then, an intelligent prediction model for cement take based on improved extreme learning machine (ELM) is proposed, which uses the grasshopper optimization algorithm (GOA) to optimize its input weights w and hidden layer thresholds b. Finally, taking sets of cement take data from a real dam foundation project as an example to verify the performance of the proposed prediction model, the results illustrates that the proposed model has good prediction accuracy and can assist grouting engineers in adjusting grouting construction design and controlling grouting quality, which has a wide application prospect.
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Cucinotta, Filippo, Marcello Raffaele, and Fabio Salmeri. "A Topology Optimization Method for Stochastic Lattice Structures." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70566-4_38.

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AbstractStochastic lattice structures are very powerful solutions for filling three-dimensional spaces using a generative algorithm. They are suitable for 3D printing and are well appropriate to structural optimization and mass distribution, allowing for high-performance and low-weight structures. The paper shows a method, developed in the Rhino-Grasshopper environment, to distribute lattice structures until a goal is achieved, e.g. the reduction of the weight, the harmonization of the stresses or the limitation of the strain. As case study, a cantilever beam made of Titan alloy, by means of SLS technology has been optimized. The results of the work show the potentiality of the methodology, with a very performing structure and low computational efforts.
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Łukasik, Szymon. "Grasshopper Optimization Algorithm." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-15.

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Reddy, Alla Divya Sree, and A. K. Vamsi Krishna Reddy. "Enhanced Grasshopper Optimization Algorithm for Numerical Optimization." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2674-6_6.

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Łukasik, Szymon. "Grasshopper Optimization Algorithm - Modifications and Applications." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422607-15.

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Sharma, Bharti, Adeel Hashmi, Gunjan Beniwal, and Charu Gupta. "Grasshopper Optimization Algorithm Based Recommender System." In Studies in Computational Intelligence. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88279-1_12.

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Singh, Sandeep, Gagan Singh, Sourav Bose, and Shiva. "FIR Filter Design Using Grasshopper Optimization Algorithm." In Recent Advances in Metrology. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2468-2_28.

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Bekana, Paulos, Archana Sarangi, Debahuti Mishra, and Shubhendu Kumar Sarangi. "Improved Grasshopper Optimization Algorithm Using Crazy Factor." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9873-6_17.

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Conference papers on the topic "Grasshopper Optimization Algorithm(GOA)"

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Shaha, Tonmoy Kanti, Md Mahbub Hosen, Md Mokarrom Hossain, Anup Kumar Das, Bidyut Baran Saha, and Sampad Ghosh. "Optimization of Energy Management Scheduling via Grasshopper Optimization Algorithm in Industries of Bangladesh." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013960.

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Liu, Wei, Guangyu Han, Tengteng Ren, Chuang Zhang, and Tong Li. "Improvement of Grasshopper Optimization Algorithm Based on Chaotic Mapping and Levy Flight." In 2024 2nd International Conference on Signal Processing and Intelligent Computing (SPIC). IEEE, 2024. http://dx.doi.org/10.1109/spic62469.2024.10691618.

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Hu, Jinghui, Zhe Zhang, and Lingjian Kong. "Driving Risk Discrimination Based on Support Vector Machine Optimized with Grasshopper Optimization Algorithm." In 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA). IEEE, 2024. http://dx.doi.org/10.1109/icipca61593.2024.10709165.

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Sarkar, Debasis. "Application of Grasshopper Optimization Algorithm for Design and Development of Net Zero Energy Residential Building in Ahmedabad, India." In 2024 International Conference on Sustainable Energy: Energy Transition and Net-Zero Climate Future (ICUE). IEEE, 2024. https://doi.org/10.1109/icue63019.2024.10795552.

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Cai, Zhongyuan, Shiqi Jiang, Jiajia Yan, et al. "A Method for Extracting Fault Features of Total Pressure in Aero Engine Based on Improved Grasshopper Optimization Algorithm." In 2024 10th Asia Conference on Mechanical Engineering and Aerospace Engineering (MEAE). IEEE, 2024. https://doi.org/10.1109/meae62008.2024.11026713.

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Umar, Buhari U., Mohammed B. Muazu, Jonathan G. Kolo, James Agajo, and Ifetola D. Matthew. "Epilepsy Detection Using Artificial Neural Network and Grasshopper Optimization Algorithm (GOA)." In 2019 15th International Conference on Electronics, Computer and Computation (ICECCO). IEEE, 2019. http://dx.doi.org/10.1109/icecco48375.2019.9043226.

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Bibi, Hina, Aftab Ahmad, Farhan Aadil, Mucheol Kim, and Khan Muhammad. "A Solution to Combined Economic Emission Dispatch (CEED) problem using Grasshopper Optimization Algorithm (GOA)." In 2020 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2020. http://dx.doi.org/10.1109/csci51800.2020.00131.

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Naik, Vishnu Gangadhara, Tagir Fabarisov, and Andrey Morozov. "Machine Learning Based Search for Access Points in Anomaly Detection Model." In ASME 2023 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/imece2023-113438.

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Abstract Heterogeneous systems are characterized by a high degree of complexity that poses challenges to conventional anomaly detection methods. One of the challenges is the production of large amounts of data where Deep Learning based Anomaly Detection Models (DLAD) have been demonstrated to outperform conventional techniques in detecting anomalies. However, DLAD models require the manual selection of Access Points, which are specific points in a system from which the training data is recorded. The selection of Access Points is a critical task that can significantly affect the performance of the anomaly detection models. It requires domain knowledge and expertise in the intricacies of the system, which is difficult to acquire and prone to human errors. As the size of the system grows, selecting Access Points becomes increasingly challenging. In this paper, we propose a new machine learning-based algorithm called the Access Point Search Algorithm (APSA). The aim of APSA is to automate the task of finding the optimal set of Access Points that can aid DLAD models in detecting anomalies in a particular system. The algorithm utilizes a special error detector that dynamically takes in multiple signals and forecasts the signal values. The objective of finding the optimal set of Access Points is formulated as a feature selection problem, which is supervised using a binary variant of the Grasshopper Optimization Algorithm (GOA). We demonstrate the feasibility and effectiveness of the proposed algorithm by deploying it in a Simulink model. We illustrate the reliability of the proposed algorithm by feeding the signals from all the Access Points in the set provided by APSA into the DLAD model one by one. The reliability is further discussed by carrying out a fault injection experiment on the Simulink model. The proposed algorithm was able to reduce 80% of the Access Points. The Access Points selected by APSA showed a high probability of detecting anomalies over the Access Points that were not selected. The results suggest that the proposed algorithm can efficiently and effectively select the optimal set of Access Points for DLAD models to detect anomalies in component signals. It offers a promising solution to automate the tedious and error-prone task of selecting Access Points, thereby reducing the domain knowledge and expertise required.
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Gao, Man, Hui Xu, Jun Su, and Lingyu Yan. "A Modified Grasshopper Optimization Algorithm." In 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2021. http://dx.doi.org/10.1109/idaacs53288.2021.9660892.

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Łukasik, Szymon, Piotr Andrzej Kowalski, Małgorzata Charytanowicz, and Piotr Kulczycki. "Data Clustering with Grasshopper Optimization Algorithm." In 2017 Federated Conference on Computer Science and Information Systems. IEEE, 2017. http://dx.doi.org/10.15439/2017f340.

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