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whale optimization algorithm coupled with machine learning models for quantitative prediction of soil Ni content

MICROCHEMICAL JOURNAL 2025年 209卷

作者： Fu, Chengbiao Feng, Xiqin Tian, Anhong Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Peoples R China Kunming Univ Sci & Technol Fac Land Resource Engn Kunming 650093 Peoples R China

Under the background of environmental protection and sustainable agricultural development, it is crucial to monitor soil heavy metals, especially potentially harmful substances such as nickel. Spectroscopy technology is characterized by non-destructiveness and high efficiency, it can rapidly monitor the nickel content in the soil and provide guidance for safe agricultural production, thus promoting the green development of agriculture. There are few reports on the quantitative prediction of soil heavy metal nickel (Ni) content based on visible and nearinfrared (Vis-NIR) spectroscopy. In this study, 60 soil samples were collected from the Mojiang Hani Autonomous County, Yunnan Province, to explore a high-precision quantitative prediction model. A feature band extraction method based on whale optimization algorithm (WOA) was proposed and analyzed in comparison with the traditional feature selection methods based on genetic algorithm (GA) and particle swarm optimization (PSO). Three machine learning models (support vector regression (SVR), random forest (RF), and back propagation (BP)) were employed to study the prediction accuracy of the quantitative prediction model, and the performance of the constructed model was evaluated based on the coefficient of determination (R2), the root mean square error (RMSE), the residual prediction bias (RPD), and the performance ratio of the quartile distance (RPIQ). The results of the study indicated that: The swarm intelligent optimization algorithm based on WOA can extract fewer feature bands. The soil feature bands extracted by WOA are reduced by 15.67 % and 14.93 % compared to the GA and PSO algorithms, respectively. For quantitative prediction of soil Ni content, machine learning models based on BP had the best accuracy, GA-BP, PSO-BP and WAO-BP performance for quantitative prediction of soil Ni content was very good, extremely good, and extremely good with RPD values of 2.523, 2.644, and 3.098, respectively. Compared to the GA-

关键词： whale optimization algorithm Machine learning Soil heavy metals Vis-NIR spectroscopy Feature variable selection

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Dynamic multi-swarm whale optimization algorithm based on elite tuning for high-dimensional feature selection classification problems

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APPLIED SOFT COMPUTING 2025年 169卷

作者： Miao, Fahui Wu, Yong Yan, Guanjie Si, Xiaomeng Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Univ Macau Zhuhai UM Sci & Technol Res Inst Zhuhai 519000 Peoples R China Shanghai Urban Construct Vocat Coll Dept Basic Educ Shanghai 201415 Peoples R China Shanghai Polytech Univ Inst Artificial Intelligence Sch Comp & Informat Engn Shanghai 201209 Peoples R China

Feature selection algorithms are crucial technologies for reducing the dimensionality of high-dimensional data. However, the exponential expansion of the decision space due to high-dimensional data makes most feature selection algorithms highly susceptible to local optima. To increase the quality of feature selection, we propose a dynamic multi-swarm whale optimization algorithm based on elite tuning (EMSWOA). First, we propose a dynamic multiple swarm construction mechanism based on the centroid distance metric, which enhances the exploitation and exploration abilities of the algorithm by improving the connectivity between sub-population topologies and preventing the algorithm from falling into precocious convergence. Secondly, to address the issue of invalid probability flips caused by random thresholds during feature space transformation, we design a novel elite tuning mechanism. This mechanism uses the high-quality information in the elite solution to assess and correct the probability of feature flipping;it effectively improves the algorithm's recognition of important features and reduces the interference of invalid probability flips in the optimization search. Finally, we validated the EMSWOA against six comparison algorithms on 19 datasets. The experimental results demonstrate that, compared with the most effective benchmark algorithms, the EMSWOA yields an average accuracy increase of 2.91 % and an average reduction in the number of features by a factor of 3.58.

关键词： whale optimization algorithm Feature selection Swarm intelligence Centroid distance metric Elite tuning strategy

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Multi-Timescale Nested Hydropower Station optimization Scheduling Based on the Migrating Particle whale optimization algorithm

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ENERGIES 2025年第7期18卷 1780-1780页

作者： Zhang, Mi Zhou, Guosheng Liu, Bei Huang, Dajun Yu, Hao Mo, Li Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Hubei Key Lab Digital Valley Sci & Technol Wuhan 430074 Peoples R China Loushui Hydropower Co Ltd Hubei Energy Grp Enshi 445800 Peoples R China Changjiang Survey Planning Design & Res Co Ltd Wuhan 430010 Peoples R China

Exploring efficient and stable solution methods for hydropower generation optimization models is crucial for enhancing reservoir power generation efficiency and achieving the sustainable use of water resources. However, existing studies predominantly focus on single-timescale scheduling models, failing to fully exploit multi-timescale runoff information. Additionally, commonly used solution algorithms often face challenges such as premature convergence, susceptibility to local optima, and dimensionality issues. To address these limitations, this paper proposes the Migrating Particle whale optimization algorithm (MPWOA), which initializes the population using chaotic mapping, incorporates a particle swarm mechanism to enhance exploitation during the spiral predation phase, and integrates the black-winged kite migration mechanism to improve stochastic search performance. Validation on classical test functions and the Jiangpinghe River of the multi-timescale nested optimal scheduling model demonstrates that MPWOA exhibits faster convergence and stronger optimization capabilities and significantly improves power generation. The multi-timescale nested scheduling scheme derived from this algorithm effectively utilizes runoff information, offering a practical and highly efficient solution for hydropower scheduling.

关键词： optimal scheduling of hydropower plants generation maximization criterion multiple timescale nesting whale optimization algorithm

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A novel multi-strategy combined whale optimization algorithm for cascade reservoir operation of complex engineering optimization

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APPLIED SOFT COMPUTING 2025年 173卷

作者： Hou, Ziqi Peng, Huichun Li, Jiqing North China Elect Power Univ Dept Math & Phys Beijing 102206 Peoples R China North China Elect Power Univ Sch Water Resources & Hydropower Engn Beijing 102206 Peoples R China

Reservoir operation optimization is a complex nonlinear problem involving multiple variables and physical constraints, making it one of the most challenging optimal issues in water resources management. The whale optimization algorithm (WOA) features a straightforward mechanism and exceptional optimization performance. However, with escalating problem complexity, problems such as premature convergence and inadequate global exploration emerge. This study proposes a multi-strategy combined whale optimization algorithm (SCWOA) to address these problems. The approach retains the powerful exploitation mechanism of WOA while implementing the following improvements: introducing parallel multiplication and division operators to enhance global exploration capability, adopting the dual-strategy encirclement mechanism to enrich population diversity, and integrating dynamic spiral mechanism to improve solution accuracy coupling the adaptive escape mechanism to reduce the local stagnation times. Subsequently, numerical experiments are conducted to compare and analyze SCWOA with seven commonly used optimization algorithms across 53 benchmark functions. The analysis results indicate that SCWOA surpasses existing algorithms in global optimization accuracy, robustness, and exploration ability when handling most complex problems with varying dimensions and modes. Furthermore, a generation operation model of a practical hydropower system in China is developed under multiple constraints, such as ice prevention, flood control, and water supply. The operation results show that the schemes of SCWOA generate higher power generation than existing algorithms under different scenarios, effectively improving hydropower utilization rates. Therefore, a novel approach is provided for solving complex reservoir operation optimization problems.

关键词： whale optimization algorithm Cascade reservoir operation Random jump search Adaptive escape Operation optimization

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Kolmogorov-Arnold networks guided whale optimization algorithm for feature selection in medical datasets

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JOURNAL OF BIG DATA 2025年第1期12卷

作者： Zheng, Boli Chen, Yi Wang, Chaofan Heidari, Ali Asghar Liu, Lei Chen, Huiling Liang, Guoxi Wenzhou Univ Inst Big Data & Informat Technol Wenzhou 325000 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Wenzhou Polytech Dept Artificial Intelligence Wenzhou 325035 Peoples R China

Feature selection (FS), dealing with pathological data with a high dimensionality and a small number of samples, has always been quite challenging. Among these, wrapper-based FS methods utilizing evolutionary algorithms have gained immense popularity due to their high accuracy. Additionally, utilizing surrogate models alongside wrapper-based FS methods can help save time by reducing the need for real model predictions. However, the use of surrogate models alongside evolutionary algorithms (EAs) presents two major challenges. Firstly, the training samples for the surrogate model all come from the iterative process of EAs. For surrogate models, the limited number of iterations in EAs results in a sparse amount of training samples. Additionally, FS emphasizes reducing feature quantity, which further contributes to the sparsity of the training data. In this paper, we propose a method for constructing training data based on the reliefF algorithm, which not only enables the acquisition of a large amount of training data but also helps in addressing the issue of feature sparsity. Furthermore, we propose the use of Kolmogorov-Arnold networks (KAN) as surrogate models to address the issue of sparse features. Finally, the whale optimization algorithm (WOA) was chosen as the evolutionary algorithm, as it has exhibited excellent performance in FS problems. Experimental results indicate that using KAN as a surrogate model to identify superior individuals generated by the WOA is feasible and has shown excellent performance in addressing medical FS problems.

关键词： Feature selection whale optimization algorithm Kolmogorov-Arnold networks Surrogate models Evolutionary algorithms

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IWOA-RNN: An improved whale optimization algorithm with recurrent neural networks for traffic flow prediction

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ALEXANDRIA ENGINEERING JOURNAL 2025年 117卷 563-576页

作者： Liu, Zhiyou Li, Xinbin Lu, Zhigang Meng, Xianhui Yanshan Univ Dept Elect Engn Key Lab Power Elect Energy Conservat & Motor Drive Qinhuangdao 066004 Peoples R China North China Univ Sci & Technol Affiliated Hosp Tangshan 063000 Peoples R China Tangshan Univ Dept Electromech Engn Tangshan 063000 Peoples R China

In order to improve the accuracy of traffic flow prediction and support the dynamic control of road management departments, this paper aims to minimize real-time traffic flow prediction errors. Therefore, highway data was cleaned and normalized, and proportionally divided into training and testing datasets. Subsequently, a traffic flow prediction model based on IWOA-RNN is proposed, which integrates the whale optimization algorithm (WOA) with Recurrent Neural Networks (RNN). To address issues such as slow convergence speed and susceptibility to local optima in the WOA algorithm, a population initialization method based on improved Tent mapping is introduced to enhance the diversity of the initial population, thereby improving the global search capability of the algorithm. Simultaneously, a dimension-wise pinhole imaging-based reverse learning strategy is employed to further enhance the local search ability and robustness of the WOA algorithm. These improvements allow more efficient optimization of RNN parameters, improving the performance and accuracy of the traffic flow prediction model. Experimental results on traffic flow data from Guangzhou highways demonstrate that: 1) The improved model significantly outperforms traditional methods in real traffic datasets;2) It improves the accuracy and robustness of traffic flow prediction, providing new directions and insights for the further development of traffic flow prediction models.

关键词： Traffic flow prediction whale optimization algorithm Recurrent Neural Network Tent chaotic mapping Pinhole imaging reverse learning

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A whale optimization algorithm-based ensemble model for power consumption prediction

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ELECTRICAL ENGINEERING 2025年第2期107卷 1659-1670页

作者： Weng, Shaoyuan Liu, Zimeng Fan, Zongwen Zhang, Guoliang Xiamen Inst Software Technol Xiamen 361024 Peoples R China Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China

As one of the indispensable energies, electricity plays an essential role in modern society. Accurate prediction of power consumption is crucial for managing energy generation and purchasing, preventing overloading, and ensuring energy generation and purchase for preventing overloading and having efficient energy storage. In this paper, we propose a whale optimization algorithm-based ensemble learning algorithm (WOA-ELA) for multi-output power consumption prediction using a weighted ensemble approach. Firstly, we optimize the hyper-parameters of the base estimators using the WOA. Each base estimator's performance is evaluated through fivefold cross-validation to mitigate the influence of data splitting during model training. After obtaining the trained estimators, we further optimize the weights that use to ensemble these base estimators for the final prediction. These weights are designed to emphasize the strengths of high-performing estimators while mitigating the impact of weaker ones. Given the three outputs of power consumption, our target is to minimize their mean absolute error (MAE) using the WOA. Experimental results show that our proposed WOA-ELA outperforms the comparative models in predicting the power consumption. The average MAE achieved by our proposed WOA-ELA surpasses the four comparison models by 19.14%, 17.17%, 10.53%, and 2.15%, respectively. Additionally, its average root mean squared error is better by 15.12%, 18.50%, 7.16%, and 1.16% compared to the four models. This indicates that the use of WOA is effective for improving the prediction performance by optimizing the hyper-parameters of each model and the weights for the ensemble operation. With the promising results, our WOA-ELA can be a useful tool for energy management systems in multi-output power control scenarios.

关键词： whale optimization algorithm Power consumption prediction Weighted ensemble strategy Hyper-parameter optimization

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PM2.5 concentration prediction using a whale optimization algorithm based hybrid deep learning model in Beijing, China

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ENVIRONMENTAL POLLUTION 2025年 371卷 125953页

作者： Wei, Qing Zhang, Huijin Yang, Ju Niu, Bin Xu, Zuxin Tongji Univ Coll Environm Sci & Engn Shanghai 200092 Peoples R China Tongji Univ Key Lab Urban Water Supply State Key Lab Pollut Control & Resource ReuseMini Water Saving & Water Environm Governance Yangtze R Shanghai 200092 Peoples R China Guangdong Inst Water Resources & Hydropower Res Guangzhou 510000 Peoples R China PowerChina East China Survey Design & Res Inst Co Hangzhou 310000 Peoples R China

PM2.5 is a significant global atmospheric pollutant impacting visibility, climate, and public health. Accurate prediction of PM2.5 concentrations is critical for assessing air pollution risks and providing early warnings for effective management. This study proposes a novel hybrid machine learning model that combines the whale optimization algorithm (WOA) with a convolutional neural network (CNN), long short-term memory (LSTM), and an attention mechanism (AM) to predict daily PM2.5 concentrations. Tested with meteorological and air pollution daily data from 2014 to 2018, the WOA-CNN-LSTM-AM model demonstrates substantial improvements. It achieves MAE, RMSE, MBE, and R2 values of 14.29, 21.96, -0.23, and 0.93, respectively, showing a reduction in prediction errors by 39% compared to CNN and 34% compared to LSTM models. In the medium-term forecast, the accuracy of the hybrid model is 30%-54% over WOA-CNN-LSTM and 26%-39% over CNN-LSTM-AM. The R2 value decreases by 2.5% from the 1-day to 5-day forecast, maintaining high accuracy. SHAP analysis reveals that NO2 and CO are the primary drivers for PM2.5 predictions. This study provides a reliable tool for short and medium-term PM2.5 prediction and air pollution control.

关键词： PM 2.5 prediction whale optimization algorithm Convolutional neural network Long short-term memory Shapley additive explanation

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A multi-objective analysis for enhanced energy and exergy performances of an integrated compressed air energy storage system using the meta-heuristic whale optimization algorithm

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JOURNAL OF ENERGY STORAGE 2025年 105卷

作者： Li, Shuguang Cai, Fei Abbas, Saleem Jasim Dutta, Ashit Kumar Shomurotova, Shirin Elmasry, Yasser Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China Al Mustaqbal Univ Coll Engn & Technol Mech Power Tech Engn Dept Babylon 51001 Iraq AlMaarefa Univ Coll Appl Sci Dept Comp Sci & Informat Syst Riyadh 13713 Saudi Arabia Tashkent State Pedag Univ Dept Chem Teaching Methods Bunyodkor St 27 Tashkent Uzbekistan King Khalid Univ Coll Sci Dept Math POB 9004 Abha 61466 Saudi Arabia

This study investigates the optimization of energy and exergy efficiencies in a compressed air energy storage integrated energy system using the meta-heuristic whale optimization algorithm. The analysis focuses on the effects of key operating parameters, including current density, utilization factor, and temperature, on the system's performance. The whale optimization algorithm identifies seven multi-objective optimum points, with processing conditions ranging from a current density of 3000 A/m2 to 5237 A/m2, utilization factors between 0.740 and 0.747, and temperatures from 700 degrees C to 900 degrees C. Desirability analysis reveals that some points, with a desirability value of 1, are the most optimal. Among these, one point characterized by a utilization factor of 0.747, a current density of 3000 A/m2, and a temperature of 700 degrees C, is identified as the optimal point. Under these conditions, the system achieves an energy efficiency of 66.63 % and an exergy efficiency of 34.85 %. The study highlights the significant potential of the meta-heuristic whale optimization algorithm in navigating complex search spaces to identify optimal processing conditions. The results underscore the importance of balancing electrical and thermal efficiencies to achieve overall system optimization using meta-heuristic optimization algorithms.

关键词： Energy storage Meta-heuristic optimization Compressed air Efficiency whale optimization algorithm

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Forest biomass carbon stock estimates via a novel approach: K-nearest neighbor-based weighted least squares multiple birth support vector regression coupled with whale optimization algorithm

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 232卷

作者： Deng, Niannian Xu, Renpeng Zhang, Ying Wang, Haoting Chen, Chen Wang, Huiru Beijing Forestry Univ Coll Sci Dept Math 35 Qinghua East Rd Beijing 100083 Peoples R China Beijing Forestry Univ Coll Sci Dept Elect Studies 35 Qinghua East Rd Beijing 100083 Peoples R China

Multiple birth support vector regression (MBSVR) provides fast computation and superior performance but overlooks local sample information and has challenges in parameter selection. The traditional least squares models boast fast computational speed but lack robustness and may struggle with noise and outliers. The carbon storage estimates are easily affected by noise and interference points. MBSVR and least squares models are only partially effective in carbon storage estimates. Consequently, we propose least squares multiple birth support vector regression (LSMBSVR) and K-nearest neighbor-based (KNN) weighted least squares multiple birth support vector regression (WLSMBSVR), which have the following merits. Firstly, both models inherit the strengths of MBSVR. Secondly, they exhibit enhanced fitting accuracy, robust stability, and remarkable anti- interference capability. Thirdly, LSMBSVR offers a faster training speed and maintains a comparable regression performance to MBSVR. Fourthly, WLSMBSVR considers the local information, enhancing its anti-interference capability. Lastly, we employ the whale optimization algorithm (WOA) to improve the effectiveness of parameter selection. Experiment results indicate that our models can be more effective on carbon storage, synthetic, and UCI datasets than compared models, verifying the broad application value of our models.

关键词： SVR K-nearest neighbor Least squares Weights whale optimization algorithm Carbon storage estimates

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