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

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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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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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 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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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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Quantum encoding whale optimization algorithm for global optimization and adaptive infinite impulse response system identification

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ARTIFICIAL INTELLIGENCE REVIEW 2025年第5期58卷 1-58页

作者： Zhang, Jinzhong Liu, Wei Zhang, Gang Zhang, Tan West Anhui Univ Sch Elect & Photoelect Engn Luan 237012 Peoples R China

The whale optimization algorithm (WOA) is motivated by the predatory nature of bubble nets and mimics dwindling and encircling, bubble net persecuting, and randomized wandering and foraging actions to locate the expansive adequate value. However, the WOA has several deficiencies: inadequate resolution accuracy, sluggish convergence speed, susceptibility to search stagnation, and insufficient localized detection efficiency. A quantum encoding WOA (QWOA) is introduced for global optimization and adaptive infinite impulse response (IIR) system identification. The quantum encoding mechanism exploits the principle of a quantum bit to encode a search agent, which manipulates the state of an essential quantum bit and amends the location data. The quantum rotation gate modulates the quantum bit's configuration, the quantum NOT gate accomplishes bit mutation and prohibits precocious convergence. The probability amplitude of the quantum bit represents the multistate superposition state of the search agent, which enriches the population diversity, advances individualized information, broadens the detection scope, inhibits premature convergence, facilitates estimation effectiveness, and promotes solution accuracy. The QWOA not only promptly locates the search scope nearest the most appropriate solution but also computes the spiral-shaped encircling route to promote predation diversification. Twenty-three benchmark functions, eight real-world engineering layouts, and adaptive IIR system identification are utilized to assess the QWOA's feasibility and effectiveness. The experimental results reveal that QWOA successfully equalizes exploration and exploitation to accelerate convergence speed, ameliorate calculation accuracy, and strengthen stability and robustness.

关键词： whale optimization algorithm Quantum encoding Benchmark functions Engineering layouts Infinite impulse response system identification

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Multi-objective optimization of cold plate with spoiler for battery thermal management system using whale optimization algorithm

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APPLIED THERMAL ENGINEERING 2025年 260卷

作者： Peng, Zeyuan Huang, Jianxun Lv, Junshuai Ye, Jiedong Wuhan Univ Technol Sch Automot Engn 205 Luoshi Rd Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Naval Architecture Ocean & Energy Power Engn Wuhan 430063 Peoples R China Univ British Columbia Sch Engn Kelowna BC V1V 1V7 Canada

With the high population of electric vehicle adoption, precisely controlling the temperature of the battery modules is essential to provide long-term sustainability and reliability. In the amount of battery thermal management techniques, adding spoilers is a promising method for enhancing the heat transfer performance of cold plates, but the performance of the system is highly sensitive to different geometry parameters. In this study, heat transfer performance for the cold plates battery thermal management system with a tesla flow channel was numerically and experimentally investigated with a focus on the geometry parameters, including spoiler length (L), mounting position (X), and coolant flow rate (nu). A modified heuristic-based swarm intelligence multiobjective optimization algorithm is proposed to obtain the optimal spoiler parameters. The results show the maximum temperature of the battery module is reduced by 3.12 degrees C after adding the spoiler. The optimization spoilers maintain the maximum temperature of the battery module below 30.9 degrees C, and the best spoiler parameters as L = 5.10 mm, X = 14.3 mm, and v = 1.186 x 10-2 m/s. This study uses a heuristic-based swarm intelligence multi-objective optimization algorithm to investigate the optimization process of the spoiler structural parameters and provides guidance for the application of advanced multi-objective optimization algorithms in cold plate design.

关键词： BTMS Cold plate channel whale optimization algorithm Spoiler Design Enhanced heat transfer

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An Improved whale optimization algorithm with Adaptive Fitness-Distance Balance

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年第2期20卷 232-243页

作者： Hou, Chunzhi Lei, Zhenyu Zhang, Baohang Yuan, Zijing Wang, Rong-Long Gao, Shangce Univ Toyama Fac Engn Toyama 9308555 Japan Univ Fukui Fac Engn Fukui 9108507 Japan

whale optimization algorithm (WOA) is a new bio-meta-heuristic algorithm presented to simulate the predatory humpback whales' behavior in the ocean. In previous studies, WOA has been observed to exhibit lower accuracy and slower convergence rates. In this paper, we propose an improved the WOA by innovatively incorporating an adaptive fitness-distance balance strategy, namely AFWOA. AFWOA can continuously and efficiently identify the maximum potential candidate solutions from the population within the search process, thus improving the accuracy rate and convergence speed of the algorithm. Through various experiments in IEEE CEC2017 and an ill-conditional problem, AFWOA is proven to be more competitive than the original WOA, several other state-of-the-art WOA variants and other four classic meta-heuristic algorithms. (c) 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： whale optimization algorithm fitness-distance balance benchmark problems selection method adaptive

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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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