Suspended sediment prediction using integrative soft computing models: on the analogy between the butterfly optimization and genetic algorithms

Fadaee, Marzieh; Mahdavi‐Meymand, Amin; Zounemat‐Kermani, Mohammad

doi:10.1080/10106049.2020.1753821

Cited by 27 publications

(6 citation statements)

References 37 publications

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“…This section presents the adaptation of the original BOA to show its performance in addressing optimization problems and achieving the best of their results. Fadaee et al [42] investigated the capability of the original BOA by comparing its performance with the genetic algorithm (GA) to increase the accuracy of machine learning models, including adaptive neuro-fuzzy inference system, multiple linear regression, and artificial neural network. Several independent parameters were added to the machine learning models to increase prediction accuracy.…”

Section: Original Butterfly Optimization Algorithmmentioning

confidence: 99%

“…The algorithm tackled a wide range of problems: FS [22,31,34,57,60,60], numerical optimization [33,37,64,70,71,75], PV models [23,47], rarly search blindness [24], energy consumption [25], image segmentation [72], scheduling [26,59], multi disease prediction [73], medical data classification [27], optimal capacity of gas production [76], sentiment analysis [28], roller burnishing process parameters [52], pilot contamination in massive systems for 5G communication networks [55] , optimum shape design [46], combined cooling heating and power generation system [62], household CO2 emissions mitigation strategies [48], solving elliptic partial differential equations [66], reliability optimization problems [65], suspended sediment prediction [42], maximum power point tracking [43,68], engineering problems [29,61,63,67], green vehicle routing problem [78], high-dimensional optimization problems [74], model predictive control [50], crowd behaviour recognition [69], structural damage detection…”

Section: Applications Of Butterfly Optimization Algorithmsmentioning

confidence: 99%

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Recent Advances in Butterfly Optimization Algorithm, Its Versions and Applications

Makhadmeh

Al-Betar

Abasi

et al. 2022

Arch Computat Methods Eng

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The butterfly optimization algorithm (BOA) is a recent successful metaheuristic swarm-based optimization algorithm. The BOA has attracted scholars' attention due to its extraordinary features. Such as the few adaptive parameters to handle and the high balance between exploration and exploitation. Accordingly, the BOA has been extensively adapted for various optimization problems in different domains in a short period. Therefore, this paper reviews and summarizes the recently published studies that utilized the BOA for optimization problems. Initially, introductory information about the BOA is presented to illustrate the essential foundation and its relevant optimization concepts. In addition, the BOA inspiration and its mathematical model are provided with an illustrative example to prove its high capabilities. Subsequently, all reviewed studies are classified into three main classes based on the adaptation form, including original, modified, and hybridized. The main BOA applications are also thoroughly explained. Furthermore, the BOA advantages and drawbacks in dealing with optimization problems are analyzed. Finally, the paper is summarized in conclusion with the future directions that can be investigated further.

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Section: Original Butterfly Optimization Algorithmmentioning

confidence: 99%

Section: Applications Of Butterfly Optimization Algorithmsmentioning

confidence: 99%

Recent Advances in Butterfly Optimization Algorithm, Its Versions and Applications

Makhadmeh

Al-Betar

Abasi

et al. 2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

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“…In the fourth layer, the output parameters of each node are determined as the product of the normalized firing strength and a first-order polynomial. Finally, in the last layer, the overall output of the fuzzy system as the summation of the incoming signals of the fourth layer is measured (Fadaee et al, 2020).…”

Section: Adaptive-neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Groundwater quality modeling: On the analogy between integrative PSO and MRFO mathematical and machine learning models

Zounemat‐Kermani

Mahdavi‐Meymand

Fadaee

et al. 2021

Environmental Quality Mgmt

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Reliable and accurate modeling of groundwater quality is an important element of sustainable groundwater management of productive aquifers. In this research, specific conductance (SC) of groundwater is predicted based on different individual and integrative machine learning, adaptive neuro-fuzzy inference system (ANFIS), and nonlinear mathematical models. For developing the integrative models, the well-known particle swarm optimization (PSO) and novel manta ray foraging optimization (MRFO) heuristic algorithms are embedded in the models. Presenting different univariate, bivariate, and multivariate input scenarios, the parameters used to develop and validate the models include groundwater level, salinity, and water temperature at an observation well near Florida City. The findings reveal that applying more independent parameters (multivariate scenario) enhances the performance of both the mathematical and machine learning models. Even though the mathematical models present an acceptable performance for the prediction of SC (index of agreement, IA, equals 0.933), the ANFIS models provide the most accurate SC predictions (IA = 0.943). Both the PSO and MRFO algorithms improved the prediction capability of the ANFIS models with, respectively, 13% and 5% for the RMSE.

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“…According to the previous researches, the majority of ML applications for streamflow prediction confirm the proper potential and accuracy of ML in comparison with the conventional statistical or conceptual models in addressing this hydrological issue (Riahi-Madvar et al 2019). Subsequently, new ideas for upgrading and enriching ML models, especially using integrative (hybrid) ML-heuristic methods, have been being developed and tested recently (Fadaee et al 2020).…”

Section: Introductionmentioning

confidence: 95%

Application of novel binary optimized machine learning models for monthly streamflow prediction

et al. 2023

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Accurate measurements of available water resources play a key role in achieving a sustainable environment of a society. Precise river flow estimation is an essential task for optimal use of hydropower generation, flood forecasting, and best utilization of water resources in river engineering. The current paper presents the development and verification of the prediction abilities of new hybrid extreme learning machine (ELM)-based models coupling with metaheuristic methods, e.g., Particle swarm optimization (PSO), Mayfly optimization algorithm (MOA), Grey wolf optimization (GWO), and simulated annealing (SA) for monthly streamflow prediction. Prediction precision of standalone ELM model was compared with two-phase optimized state-of-the-arts models, e.g., ELM–PSO, ELM–MOA, ELM–PSOGWO, and ELM–SAMOA, respectively. Hydro-meteorological data acquired from Gorai and Padma Hardinge Bridge stations at Padma River Basin, northwestern Bangladesh, were utilized as inputs in this study to employ models in the form of seven different input combinations. The model’s performances are appraised using Nash–Sutcliffe efficiency, root-mean-square-error (RMSE), mean absolute error, mean absolute percentage error and determination coefficient. The tested results of both stations reported that the ELM–SAMOA and ELM–PSOGWO models offered the best accuracy in the prediction of monthly streamflows compared to ELM–PSO, ELM–MOA, and ELM models. Based on the local data, the ELM–SAMOA reduced the RMSE of ELM, ELM–PSO, ELM–MOA, and ELM–PSOGWO by 31%, 27%, 19%, and 14% for the Gorai station and by 29%, 27%, 19%, and 14% for Padma Hardinge bridge station, in the testing stage, respectively. In contrast, based on external data, ELM–PSOGWO improves in RMSE of ELM, ELM–PSO, ELM–MOA, and ELM–SAMOA by 20%, 5.1%, 6.2%, and 4.6% in the testing stage, respectively. The results confirmed the superiority of two-phase optimized ELM–SAMOA and ELM–PSOGWO models over a single ELM model. The overall results suggest that ELM–SAMOA and ELM–PSOGWO models can be successfully applied in modeling monthly streamflow prediction with either local or external hydro-meteorological datasets.

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Suspended sediment prediction using integrative soft computing models: on the analogy between the butterfly optimization and genetic algorithms

Cited by 27 publications

References 37 publications

Recent Advances in Butterfly Optimization Algorithm, Its Versions and Applications

Recent Advances in Butterfly Optimization Algorithm, Its Versions and Applications

Groundwater quality modeling: On the analogy between integrative PSO and MRFO mathematical and machine learning models

Application of novel binary optimized machine learning models for monthly streamflow prediction

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