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improved whale optimization algorithm with LSTM for Stock Index Prediction

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第1期16卷 283-295页

作者： Sun, Yu Mutalib, Sofianita Tian, Liwei Guangdong Univ Sci & Technol Sch Management Dongguan Guangdong Peoples R China Univ Teknol MARA Coll Comp Informat & Math Sch Comp Sci Shah Alam Selangor Malaysia Guangdong Univ Sci & Technol Sch Comp Dongguan Guangdong Peoples R China

After the COVID-19 pandemic, the global economy began to recover. However, stock market fluctuations continue to affect economic stability, making accurate predictions essential. This study proposes an improved whale optimization algorithm (IWOA) to optimize the parameters of the Long Short-Term Memory (LSTM) model, thereby enhancing stock index predictions. The IWOA improves upon the traditional whale optimization algorithm (WOA) by integrating logistic chaotic mapping to increase population diversity and prevent premature convergence. Additionally, it incorporates a dynamic adjustment mechanism to balance global exploration and local exploitation, thus boosting optimization performance. Experiments conducted on five representative global stock indices demonstrate that the IWOA-LSTM model achieves higher accuracy and reliability compared to WOA-LSTM, LSTM, and RNN models. This highlights its value in predicting complex time-series data and supporting financial decision-making during economic recovery.

关键词： Long short-term memory network chaotic mapping dynamic adjustment mechanism improved whale optimization algorithm financial time series forecasting

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Research on the Optimal Scheduling Model of Energy Storage Plant Based on Edge Computing and improved whale optimization algorithm

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Energy Engineering 2025年第3期122卷 1153-1174页

作者： Zhaoyu Zeng Fuyin Ni School of Electrical and Information Engineering Jiangsu University of TechnologyChangzhou213001China Jiangsu Key Laboratory of Power Transmission&Distribution Equipment Technology Jiangsu University of TechnologyChangzhou213001China

Energy storage power plants are critical in balancing power supply and ***,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy *** tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network *** architecture effectively reduces transmission costs by minimizing data travel *** addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation *** IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal *** results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and *** successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy *** edge-computing framework significantly reduces transmission delays between energy ***,IWOA outperforms traditional algorithms in optimizing the objective function.

关键词： Energy storage plant edge computing optimal energy scheduling improved whale optimization algorithm

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Uncertain utility portfolio optimization based on two different criteria and improved whale optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 268卷

作者： Xu, Jiajun Li, Bo Nanjing Univ Aeronaut & Astronaut Sch Econ & Management Nanjing 211106 Peoples R China Nanjing Univ Finance & Econ Sch Appl Math Nanjing 210023 Peoples R China

Since Markowitz introduced the mean-variance model, many investors have described investment return rates by assuming they are stochastic or fuzzy variables. However, the inherent complexity and unpredictability of financial markets often render these assumptions insufficient. To address these challenges, an increasing number of researchers are exploring portfolio optimization within the framework of uncertainty theory. This paper proposes two portfolio optimization models that incorporate investors' utility under the criteria of expected value and optimistic value. We derive the deterministic forms of these two models under the assumption that the variables follow uncertain normal distributions. Additionally, we compare the differences between the multi-factor expected value-standard deviation utility (ESU) model and the optimistic value- standard deviation utility (OSU) model in terms of their ability to maximize investors' utility. To solve these models effectively, we propose an improved whale optimization algorithm (IWOA) based on the Levy flight strategy, adaptive position weight strategy, and adaptive probability threshold. Extensive numerical experiments validate the effectiveness of the improved algorithm and compare the differences between the two models.

关键词： Portfolio optimization Uncertainty theory improved whale optimization algorithm Optimistic value Investors' utility

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A comprehensive wind power prediction system based on correct multiscale clustering ensemble, similarity matching, and improved whale optimization algorithm-A case study in China

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RENEWABLE ENERGY 2025年 243卷

作者： Yu, Chunsheng Northwest Normal Univ Coll Math & Stat Lanzhou 730070 Peoples R China

As an important renewable energy source, wind energy is significant for realizing energy transition and reducing carbon emissions. With the increasing penetration of wind energy in the global energy system, higher prediction accuracy is needed to ensure the safe and stable operation of the power grid. However, the existing wind power prediction methods are constantly pursuing model improvement, ignoring the importance of data quality to the prediction performance, resulting in a stagnation of the upper limit of prediction accuracy. In this paper, we establish a comprehensive wind power prediction system based on correct multi-scale clustering ensemble, similarity matching, and an improved whale optimization algorithm. Firstly, multiple classification algorithms combined with meteorological data are used to correct the extreme scenarios in the clustering results. Secondly, a library of typical fluctuation patterns is established based on the clustering ensemble results, and the optimal training dataset is determined by similarity matching. Finally, complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) is used to extract further the power data's local features and time-frequency characteristics and to predict the modal components using the improved whale optimization algorithm(IWOA)-optimized BiLSTM network. The results of the three sets of experiments show that the proposed model is able to improve more than 10% in terms of MAE, RMSE, and MAPE compared to other models, and the model robustness is high.

关键词： Wind power Classification algorithm correction Clustering ensemble Similarity matching improved whale optimization algorithm Prediction system

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Predicting CaO activity in multiple slag system using improved whale optimization algorithm and categorical boosting

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SCIENTIFIC REPORTS 2025年第1期15卷 1-11页

作者： Xin, Zi-cheng Zhang, Jiang-shan Liu, Qing Univ Sci & Technol Beijing State Key Lab Adv Met Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

The activity of slag components is one of the primary factors influencing the thermodynamic properties of slag. In this study, a feasible model was established to predict the a(CaO) using improved whale optimization algorithm (IWOA) and Categorical Boosting (CatBoost). The effects of other variables on a(CaO) were listed in descending order of influence as follows: w(CaO), w(SiO2), temperature, w(MgO), and w(Al2O3). And the IWOA-CatBoost model achieved the highest R2 value of 0.9200, lowest RMSE of 0.0042, and lowest MAE of 0.0030 in predicting the a(CaO). The performance of the optimal IWOA-CatBoost model was evaluated and compared with that of known models. The results demonstrate that the IWOA-CatBoost model outperformed existing models and methods, such as the Factsage, ion and molecule coexistence theory, and genetic algorithm-backpropagation neural network. The accurate calculation of slag component activity is of great significance to the analysis of the thermodynamic properties of slag. Meanwhile, the approach and algorithm used to develop the a(CaO) prediction model can also be applied to predicting the activity of other slag components or other metallurgical applications (e.g., predicting molten steel temperature, steel composition, and alloy yield).

关键词： Multiple slag system a(CaO) FactSage Ion and molecule coexistence theory improved whale optimization algorithm Categorical boosting

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improved whale optimization algorithm for 2D-Otsu image segmentation with application in steel plate surface defects segmentation

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第4期17卷 1653-1659页

作者： Xie, Qiyue Zhou, Wenqian Ma, Lin Chen, Zhisheng Wu, Wanneng Wang, Xiaoli Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha 410114 Peoples R China Changsha Univ Sci & Technol Sch Energy & Power Engn Changsha 410114 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

Since the steel plate surface defect image often has complicated background and lots of noise, the segmentation accuracy is low when using the single threshold Otsu method. Therefore, this paper introduces the whale optimization algorithm (WOA) to optimize the threshold of the dual-threshold image segmentation. To avoid the premature convergence, slow convergence speed and easy fall into the local optimum of the original WOA, an improved WOA is proposed. Firstly, the WOA is discretized by using round function;secondly, the sin mapping generation chaotic sequence is used to replace the randomly generated initial population in the initialization process of the WOA to enhance the multiformity of population;thirdly, the global search and local development capabilities are balanced and improved by nonlinear time-varying factors and inertia weights in the position updating mechanism;finally, the improved WOA is applied to the two-dimensional Otsu (2D-Otsu) algorithm to select the optimal threshold for image segmentation. The simulation results of 8 classic benchmark functions show that the improved WOA can obtain the optimal value of the function 0, - 12,569.5. The improved WOA can raise convergence speed and improve the global search ability and get rid of the local optimum. The experimental results show that the proposed algorithm outperforms the Otsu algorithm and can achieve more accurate segmentation of steel plate surface defect image. Compared with 2D-Otsu algorithm, the proposed algorithm reduces running time by 0.34 s and has the highest segmentation efficiency for rolled-in scale defects.

关键词： Image segmentation improved whale optimization algorithm Steel plate surface defects Otsu algorithm

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IWOA-PTS: An improved whale optimization algorithm-based PTS technique for PAPR reduction in OFDM systems

Journal of Engineering Research (Kuwait)

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Journal of Engineering Research (Kuwait) 2025年

作者： Taba, Somia Benchana, Mohamed Amine Redadaa, Salah Ikni, Samir Laboratoire des Télécommunications (LT) Université 8 Mai 1945 Guelma 24000 Algeria

The ordinary partial transmission sequence (OPTS) technique is known as an inherent and accurate peak-to-average power ratio (PAPR) reduction scheme in orthogonal frequency division multiplexing (OFDM). However, it suffers from a major drawback, an exponentially increasing complexity, mainly caused by the exhaustive search for the ideal set of phase factors that effectively reduces PAPR. This work proposes a new PTS scheme based on an improved whale optimization algorithm (IWOA) to search for an appropriate combination of phase factors that mitigates PAPR with much lower complexity. The particular hunting behavior of humpback whales is mimicked, and its mathematical model is used in the PTS technique. The modified mutualism phase in the algorithm balances its exploration-exploitation capabilities, ensuring convergence to the best global solution and avoiding less efficient solutions. The simulation results confirm the superiority of IWOA-PTS in terms of computational load and PAPR reduction © 2025 The Authors

关键词： Computational complexity improved whale optimization algorithm OFDM PAPR reduction Partial transmit sequence Time consumption

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improved whale optimization algorithm for Transient Response, Robustness, and Stability Enhancement of an Automatic Voltage Regulator System

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ENERGIES 2022年第14期15卷 5037页

作者： Habib, Salman Abbas, Ghulam Jumani, Touqeer A. Bhutto, Aqeel Ahmed Mirsaeidi, Sohrab Ahmed, Emad M. Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Mehran Univ Engn & Technol Dept Elect Engn SZAB Campus Khairpur Mirs 66020 Pakistan Mehran Univ Engn & Technol Dept Mech Engn SZAB Campus Khairpur Mirs 66020 Pakistan Beijing Jiaotong Univ Sch Elect Engn Beijing 100044 Peoples R China Jouf Univ Coll Engn Dept Elect Engn Sakaka 72388 Saudi Arabia Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt

The proportional integral derivative (PID) controller is one of the most robust and simplest configuration controllers used for industrial applications. However, its performance purely depends on the tuning of its proportional (K-P), integral (K-I) and derivative (K-D) gains. Therefore, a proper combination of these gains is primarily required to achieve an optimal performance of the PID controllers. The conventional methods of PID tuning such as Cohen-Coon (CC) and Ziegler-Nichols (ZN) generate unwanted overshoots and long-lasting oscillations in the system. Owing to the mentioned problems, this paper attempts to achieve an optimized combination of PID controller gains by exploiting the intelligence of the whale optimization algorithm (WOA) and one of its recently introduced modified versions called improved whale optimization algorithm (IWOA) in an automatic voltage regulator (AVR) system. The stability of the IWOA-AVR system was studied by assessing its root-locus, bode maps, and pole/zero plots. The performance superiority of the presented IWOA-AVR design over eight of the recently explored AI-based approaches was validated through a comprehensive comparative analysis based on the most important transient response and stability metrics. Finally, to assess the robustness of the optimized AVR system, robustness analysis was conducted by analyzing the system response during the variation in the time constants of the generator, exciter, and amplifier from -50% to 50% range. The results of the study prove the superiority of the proposed IWOA-based AVR system in terms of transient response and stability metrics.

关键词： whale optimization algorithm improved whale optimization algorithm automatic voltage regulator stability dynamic response enhancement

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PASSENGER FLOW FORECASTING OF ZHENGZHOU METRO LINE ONE BASED ON AN improved whale optimization algorithm

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THERMAL SCIENCE 2024年第3A期28卷 2315-2322页

作者： He, Dasi Gou, Yupeng Yu, Weizhi Xia, Sanxian Lan, Jie Zhongyuan Univ Technol Sch Energy & Environm Zhengzhou Peoples R China China Railway Siyuan Survey & Design Grp Co Ltd Urban Rail & Underground Engn Design & Res Inst Wuhan Peoples R China Zhengzhou Rail Transit Co Ltd Tech Management Dept Zhengzhou Peoples R China

With the continuous expansion of urban scale in China, the increase of passenger flow has brought great pressure to the urban public transport system. An accurate and timely prediction of the short-term passenger flow at each metro station is extremely important for the metro intelligent control system to make a timely decision. In this paper, based on the measured passenger flow data of Zijingshan station of Zhengzhou Metro Line 1, an improved whale optimization algorithm is proposed to predict the passenger flow on different time scales. The results show that the method has higher accuracy than the traditional least squares support vector machines algorithm. The paper opens a new window for nowcasting warning in the rush hour and long -period optimization of the public transport.

关键词： metro passenger flow empirical mode decomposition improved whale optimization algorithm

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Prediction of End-point RH Refining Furnace Based on improved whale optimization algorithm and Stochastic Configuration Network

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METALLURGIA ITALIANA 2024年第2期115卷 18-31页

作者： Shi, C. Sun, P. Zhou, T. Wang, B. Wang, Y. Zhang, L. China Liaoning Prov Engn Res Ctr Robot Drive & Co Liaoning Inst Sci & Technot Sch Elect & Automat Engn Shenyang Peoples R China Liaoning Inst Sci & Technol Sch Elect & Automat Engn Benxi Peoples R China Benxi Beiying Steet Grp Co Benxi Peoples R China

A model for predicting the end-point temperature and end-point carbon content of RH refining steel based on an improved whale optimization algorithm and a stochastic configuration network (LWOA-SCN) is proposed to solve the existing problem of inaccurate detection of the steel composition in the ladle during the steelmaking process. The algorithm has an implicit layer structure that can be generated adaptively based on the training effect and has the ability of strong generalization performance, simple structure, fast convergence, high accuracy, and jumping out of local optimum. Firstly, the LWOA-SCN algorithm is used to construct the prediction model. Secondly, the model was tested against RBF, GA-BP, and PSO-SVM models, and the results showed that the LWOA-SCN model had the highest predicted effect. Finally, the LWOA-SCN model was tested in industrial production applications, and the results showed that the hit rate is 90.6%, 95.6%;93.7%, 94.3% for refining end-point temperature and end-point carbon content error within +/- 5 degrees C, +/- 10 degrees C;and +/- 0.005%, +/- 0.01%, respectively. which can well meet the practical needs of a steel mill. The model provides theoretical guidance and production guidance for studying the control of refining end-point temperature and end-point carbon content.

关键词： RH REFINING STOCHASTIC CONFIGURATION NETWORK LEVY FLIGHT algorithm improved whale optimization algorithm END-POINT TEMPERATURE AND END-POINT CARBON CONTENT PREDICTION MODEL

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