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Ultrasound supported synthesis of waste mangifera indica linn biodiesel: an optimization using whale algorithm

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 1695-1709页

作者： Arumugam, S. Peddamangari Venkatesulu Reddy, Chengareddy Arulvalavan, Tamilarasan Gopalasamy, Sriram Sri Chandrasekharendra Saraswathi Viswa Mahavidyal Dept Mech Engn Kanchipuram 631561 Tamil Nadu India

The synthesis of biodiesel from vegetable oil is not the right choice as its feedstock and production cost is expensive than diesel. To address this, the study reports the implementation of whale algorithm (WA) for the optimization of biodiesel synthesis from cost-effective waste feedstock, i.e. waste mango seed kernel oil (WMS) using ultrasound-supported methanolysis process. The influence of methanolysis process variables, i.e. methanol:WMS molar ratio (12:1-20:1), sonication time (10-30 min), heterogeneous catalyst concentration (1-3 wt.%), and ultrasound amplitude (25-75%) on the yield percentage of WMS methyl ester were examined and optimized. The WA model is used to augment the yield percentage upto 90.88% at optimum methanol:WMS molar ratio of 16.32:1, the sonication time of 19.75 min, the quantity of heterogeneous catalyst of 2.28 wt.% and an ultrasound amplitude of 54.31%. 1H-NMR spectral approach was adopted to ensure the formation of WMS methyl ester. The combined use of heterogeneous catalyst along with ultrasonication significantly improved the conversion percentage of WMS methyl ester to 90.88% over the conventional transesterification process yield of 83%. The outcome of this study confirmed that the optimization by WA showed marginal improvement in yield percentage of WMS methyl ester as compared to those obtained using response surface methodology (RSM), a commonly applied optimization method.

关键词： Biodiesel waste feedstock waste mango seed kernel oil whale algorithm ultrasound energy

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Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第2期28卷 1596-1611页

作者： Ehteram, Mohammad Ahmed, Ali Najah Latif, Sarmad Dashti Huang, Yuk Feng Alizamir, Meysam Kisi, Ozgur Mert, Cihan El-Shafie, Ahmed Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Univ Tenaga Nasl UNITEN Inst Energy Infrastruct IEI Kajang 43000 Selangor Malaysia Univ Tenaga Nasl UNITEN Coll Engn Dept Civil Engn Kajang 43000 Selangor Malaysia Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Dept Civil Engn Kajang 43000 Selangor Malaysia Islamic Azad Univ Dept Civil Engn Hamedan Branch Hamadan Hamadan Iran Ilia State Univ Dept Civil Engn Tbilisi Georgia Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Int Black Sea Univ Fac Comp Technol & Engn Tbilisi Georgia Univ Malaya UM Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ UAEU Natl Water Ctr Al Ain 15551 U Arab Emirates

There is a need to develop an accurate and reliable model for predicting suspended sediment load (SSL) because of its complexity and difficulty in practice. This is due to the fact that sediment transportation is extremely nonlinear and is directed by numerous parameters such as rainfall, sediment supply, and strength of flow. Thus, this study examined two scenarios to investigate the effectiveness of theartificial neural network(ANN) models and determine the sensitivity of the predictive accuracy of the model to specific input parameters. The first scenario proposed three advanced optimisers-whale algorithm (WA), particle swarm optimization (PSO), and bat algorithm (BA)-for the optimisation of the performance of artificial neural network (ANN) in accurately predicting the suspended sediment load rate at the Goorganrood basin, Iran. In total, 5 different input combinations were examined in various lag days of up to 5 days to make a 1-day-ahead SSL prediction. Scenario 2 introduced a multi-objective (MO) optimisation algorithm that utilises the same inputs from scenario 1 as a way of determining the best combination of inputs. Results from scenario 1 revealed that high accuracy levels were achieved upon utilisation of a hybrid ANN-WA model over the ANN-BA with an RMSE value ranging from 1 to 6%. Furthermore, the ANN-WA model performed better than the ANN-PSO with an accuracy improvement value of 5-20%. Scenario 2 achieved the highestR(2)when ANN-MOWA was introduced which shows that hybridisation of the multi-objective algorithm with WA and ANN model significantly improves the accuracy of ANN in predicting the daily suspended sediment load.

关键词： Soft computing model whale algorithm ANN Suspended sediment load Optimisation Iran

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Microgrid Fault Diagnosis Based on whale algorithm Optimizing Extreme Learning Machine

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2024年第3期19卷 1827-1836页

作者： Wu, Zhongqiang Lu, Xueqin Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China

A microgrid fault diagnosis method based on whale algorithm optimizing extreme learning machine (ELM) is proposed. Firstly, the three-phase fault voltage is analyzed by wavelet packet decomposition, and the feature vector composed of wavelet packet energy entropy is calculated as data samples. Then, a whale algorithm is used to optimize the extreme learning machine to establish a diagnostic model to identify and diagnose the fault type of microgrid. The whale algorithm has the characteristics of simple parameter setting, fast learning speed, and strong global optimization ability. The whale algorithm is used to optimize the input weights and hidden layer neuron thresholds of the extreme learning machine, which solves the problem that the random initialization of the input weights and hidden layer neuron thresholds easily affects the network performance, which can further improve the learning speed and generalization ability of the network, and benefit to global optimization. Simulation results show that compared with BP neural network, RBF neural network and ELM, the fault diagnosis model based on whale algorithm optimization extreme learning machine has faster learning speed, stronger generalization ability and higher recognition accuracy.

关键词： Microgrid Wavelet packet decomposition Extreme learning machine whale algorithm Fault diagnosis

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Using Response Surface Methodology and providing a modified model using whale algorithm for estimating the compressive strength of columns confined with FRP sheets

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CONSTRUCTION AND BUILDING MATERIALS 2018年 183卷 163-170页

作者： Moodi, Yaser Mousavi, Seyed Roohollah Ghavidel, Alireza Sohrabi, Mohammad Reza Rashki, Mohsen Univ Sistan & Baluchestan Civil Engn Dept Zahedan Iran Univ Sistan & Baluchestan Dept Architecture Engn Zahedan Iran

This study presented a modified model for determining the compressive strength of square and rectangular concrete columns using whale algorithm and a widespread database. In this model, the effective strain coefficient of FRP and cross section coefficient considered as an individual coefficient. Furthermore, the coefficients of the rising strength for specimens with unconfined compressive strength in upper and lower of 35 MPa are differently considered. In addition, Response Surface Methodology used for estimating the compressive strength of the confined specimens by FRP. Finally, results compared with the existing models. The predictions of the modified model and RSM show satisfactory estimations. So that RSM and the modified model have increased R-2 approximately 34% and 26% rather other models, respectively. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Response Surface Methodology Columns FRP confinement Compressive strength Strengthening whale algorithm

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Software fault prediction using whale algorithm with genetics algorithm

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SOFTWARE-PRACTICE & EXPERIENCE 2021年第5期51卷 1121-1146页

作者： Alsghaier, Hiba Akour, Mohammed Yarmouk Univ Dept Informat Syst Irbid Jordan

Software fault prediction became an essential research area in the last few years, there are many prediction and optimization techniques that have been developed for fault prediction. In this paper, an approach is developed by integrating genetics algorithm with support vector machine (SVM) classifier and whale optimization algorithm for software fault prediction. The developed approach is applied to 24 datasets (12-NASA MDP and 12-Java open-source projects), where NASA MDP is considered as a large-scale dataset, and Java open source projects are considered as a small-scale dataset. Results indicate that integrating Genetics algorithm with SVM and whale algorithm improves the performance of the software fault prediction process when it is applied to large-scale and small-scale datasets and overcome the limitations that appeared in the previous studies.

关键词： fault prediction genetics algorithm machine learning optimization algorithms whale algorithm

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Integrating the whale algorithm with Tabu search for quadratic assignment problem: A new approach for locating hospital departments

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APPLIED SOFT COMPUTING 2018年 73卷 530-546页

作者： Abdel-Basset, Mohamed Manogaran, Gunsekaran El-Shahat, Doaa Mirjalili, Seyedali Zagazig Univ Fac Comp & Informat Dept Operat Res Zagazig Egypt Univ Calif Davis Davis CA 95616 USA Griffith Univ Sch Informat & Commun Technol Nathan Campus Brisbane Qld 4111 Australia

The Quadratic Assignment Problem (QAP) is a combinatorial NP-hard optimization problem that is not solvable in a polynomial time. It has a large number of real-world applications in diverse fields (e.g. facility arrangement in a hospital). The whale Optimization algorithm is a new meta-heuristic that achieves a great success in solving the continuous problems. In this paper, we propose a memetic algorithm using the whale optimization algorithm (WA) Integrated with a Tabu Search (WAITS) for solving QAP. In fact, this work employs Tabu Search to improve the quality of solution obtained by WA for QAP problem as a local search algorithm. This is an attempt to improve the convergence speed and local search of WA as its main drawbacks. Due to the combinatorial nature of QAP, the continuous values generated from the standard WA were converted to discrete values by the largest real value mapping. The WAITS algorithm is enhanced by a local search that defines a set of neighborhood solutions to improve the accuracy of the obtained solutions. Fourteen different case studies including 122 test problems are employed for analyzing the performance of the proposed WAITS. The results show that the proposed memetic algorithm finds near-optimal solutions with an acceptable computational time. WAITS is compared to several algorithms in the literature. The results show that the proposed algorithm outperforms similar algorithms in the literature. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Quadratic assignment problem whale algorithm Tabu search Local search Hospital facility layout Combinatorial problems Facilities

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Proposing Models for Estimating the Compressive Strength of HSC and UHSC FRP-Confined Circular Concrete by Using whale algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2022年第1期46卷 67-76页

作者： Ghasemi, Hossien Sohrabi, Mohammad Reza Moodi, Yaser Univ Sistan & Baluchestan Civil Engn Dept Zahedan Iran

Confining is a very common method of reinforcing concrete columns, and numerous experiments have been performed on different-strength RC columns confined with FRP sheets. Since models proposed to determine the compressive strength of normal-strength (less than 50 MPa) concretes are many, but those of higher strength ones (greater than 50 MPa) are few, this study has collected a large set of laboratory data from different FRP-confined circular specimens with strengths greater than 50 MPa and has used the data to present modified models to determine their compressive strength. The FRP strain efficiency is defined as the ratio of the actual FRP hoop rupture strain in confined columns to that obtained from the coupon test. These models integrate the FRP strain efficiency and strength increase factors and consider the integration as a function of: (1) strain ratio, (2) confinement stiffness ratio, and (3) the combination of the latter two. These functions' constant coefficients were so calculated by the whale optimization algorithm as to minimize the difference between the models and experimental results. A comparison of the results showed that the proposed models in this study perform better than the models of previous studies for estimating compressive strength of FRP-confined circular specimens with compressive strengths greater than 50 MPa. Also, among the models presented in this study, the model that used the integrated factor as a combinatory function estimated the columns' compressive strength better so that the correlation coefficient (R-2) of this model was 89%.

关键词： High-strength concrete Confining Fiber-reinforced polymers Compressive strength whale algorithm Circular section

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Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by whale algorithm

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NATURAL RESOURCES RESEARCH 2020年第5期29卷 3233-3252页

作者： Banadkooki, Fatemeh Barzegari Ehteram, Mohammad Ahmed, Ali Najah Teo, Fang Yenn Fai, Chow Ming Afan, Haitham Abdulmohsin Sapitang, Michelle El-Shafie, Ahmed Payam Noor Univ Agr Dept Tehran Iran Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan *** Iran Univ Tenaga Nas Inst Energy Infrastruct IEI Selangor 43000 Malaysia Univ Nottingham Malaysia Fac Sci & Engn Semenyih 43500 Selangor Malaysia Univ Tenaga Nas Inst Sustainable Energy ISE Selangor 43000 Selangor Malaysia Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Univ Tenaga Nas Dept Civil Engn Selangor 43000 Selangor Malaysia Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur Malaysia

The present study attempted to predict groundwater levels (GWL) obtained from precipitation and temperature data based on various temporal delays. The radial basis function (RBF) neural network-whale algorithm (WA) model, the multilayer perception (MLP-WA) model, and genetic programming (GP) were used to predict GWL. The objectives were: (1) to prepare robust hybrid ANN models;(2) to study the combination of ANN models and optimization algorithms;and (3) to study uncertainty related to the input parameters of the models, whereby three scenarios with different inputs were considered. The results showed that for the first scenario, in which the input data were just the average of the region temperature and three temporal delays of 3, 6, and 9 months were considered, the models based on the three simultaneous temperature inputs with mentioned delays had higher performance as compared to the inputs just belonging to temperature input. The MLP-WA model was the best model among all. For the test stage, the mean absolute error of the MLP-WA model decreased to 30% and from 31 to 38% as compared to the radial basis function-whale algorithm (RBF-WA) and GP models, respectively. The second scenario was the evaluation of the predicted GWL based on the precipitation data of 3, 6, and 9 months. The results showed that the three variations of precipitation data as simultaneous input improved the models' performance. The third scenario was considered in which the data from average precipitation and temperature were simultaneously used. The best results were obtained when the precipitation and temperature data with delays of 3, 6, and 9 months were used as input.

关键词： Groundwater Water resources management whale algorithm Neural network

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Improved whale algorithm and its application in cobot excitation trajectory optimization

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INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2022年第4期6卷 615-624页

作者： Zhao, Yuntao Chen, Jun Li, Weigang Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Minist Educ Engn Res Ctr Met Automat & Measurement Technol Wuhan 430081 Peoples R China

The improved whale algorithm is proposed to design the excitation trajectory in the parameter identification of collaborative robots. Firstly, the whale algorithm is improved, the initial population is optimized by the method of uniform distribution, the adaptive weight is introduced to enhance the optimization ability of the algorithm, and the optimization speed of the algorithm is improved by using the convergence factor. Then establish the dynamic model of the robotic arm, design the excitation trajectory of the collaborative robot, and use the improved whale algorithm to optimize the excitation trajectory. Finally, the HM06 robot is used as the experimental object to run the excitation trajectory optimized by the improved whale algorithm. The experimental results show that the excitation trajectory optimized by the improved whale algorithm is smooth and has strong anti-noise ability, which is beneficial to improve the accuracy of robot parameter identification.

关键词： Dynamic model Parameter identification Excitation trajectory whale algorithm

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Frequency based feature selection method using whale algorithm

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GENOMICS 2019年第6期111卷 1946-1955页

作者： Nematzadeh, Hossein Enayatifar, Rasul Mahmud, Maqsood Akbari, Ebrahim Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran Islamic Azad Univ Dept Comp Engn Firoozkooh Branch Firoozkooh Iran Imam Abdulrahman Bin Faisal Universiry IAU Dept Management Informat Syst Coll Business Adm Dammam Saudi Arabia

Feature selection is the problem of finding the best subset of features which have the most impact in predicting class labels. It is noteworthy that application of feature selection is more valuable in high dimensional datasets. In this paper, a filter feature selection method has been proposed on high dimensional binary medical datasets - Colon, Central Nervous System (CNS), GLI_85, SMK_CAN_187. The proposed method incorporates three sections. First, whale algorithm has been used to discard irrelevant features. Second, the rest of features are ranked based on a frequency based heuristic approach called Mutual Congestion. Third, majority voting has been applied on best feature subsets constructed using forward feature selection with threshold tau = 10. This work provides evidence that Mutual Congestion is solely powerful to predict class labels. Furthermore, applying whale algorithm increases the overall accuracy of Mutual Congestion in most of the cases. The findings also show that the proposed method improves the prediction with selecting the less possible features in comparison with state of the arts. https://***/hnematzadeh

关键词： Feature selection whale algorithm Mutual congestion

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