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Monthly runoff prediction using gated recurrent unit neural network based on variational modal decomposition and optimized by whale optimization algorithm

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ENVIRONMENTAL EARTH SCIENCES 2024年第2期83卷 72页

作者： Wang, Wen-chuan Wang, Bo Chau, Kwok-wing Zhao, Yan-wei Zang, Hong-fei Xu, Dong-mei North China Univ Water Resources & Elect Power Coll Water Resources Zhengzhou 450046 Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China

To further increase the forecast precision of non-stationary non-linear monthly runoff series and improve the effectiveness of pretreatment of monthly runoff series, the whale optimization algorithm (WOA) is introduced to optimize the variational mode decomposition (VMD), and the WOA-VMD-GRU prediction model is constructed by coupling with the gating cycle unit (GRU) neural network. First, the variation modal decomposition is optimized by the whale optimization algorithm, to find the best decomposition modal number k and penalty factor alpha, then several IMF components are obtained according to VMD processing runoff sequences;finally, results are obtained by adding those of each component. Taking Manwan Hydropower, Hongjiadu Hydropower, and Changshui Hydrological Station as examples, the BP model, the GRU model, the EMD-GRU model, the CEEMDAN-GRU model, and the VMD-GRU model are compared. Four quantitative indexes were used to estimate the model performance. The results show that the proposed WOA-VMD-GRU model has the best prediction accuracy, with correlation coefficients and Nash coefficients above 0.99 and 0.97 in the prediction results of the three hydrological stations, respectively, and avoids the low efficiency of VMD decomposition parameters in manual trial computation, providing a new way for monthly runoff prediction.

关键词： whale optimization algorithm Variational mode decomposition Gated recurrent unit neural network Monthly runoff forecasting

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A newly developed hybrid method on pavement maintenance and rehabilitation optimization applying whale optimization algorithm and random forest regression

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INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING 2023年第2期24卷

作者： Naseri, Hamed Jahanbakhsh, Hamid Foomajd, Amirabbas Galustanian, Narek Karimi, Mohammad M. D. Waygood, E. O. Ecole Polytech Montreal Univ Dept Civil Engn Montreal PQ Canada Amirkabir Univ Technol Dept Civil & Environm Engn Tehran Iran Univ Sci & Culture Dept Civil Engn Tehran Iran Southern Illinois Univ Edwardsville Sch Engn Carbondale IL USA Tarbiat Modares Univ Dept Civil & Environm Engn Tehran Iran

Developing an accurate pavement prediction model plays a dominant role in pavement M&R optimization. Despite employing different robust machine learning techniques to predict pavement conditions, these methods have some weaknesses in synchronising with exact optimization algorithms. The main contribution of this study is to propose a novel method for optimizing the pavement M&R plan with high accuracy. Contrary to conventional approaches, a robust prediction algorithm, Random Forest Regression (RFR), is applied to predict the pavement International Roughness Index (IRI). In addition, Multiple Linear Regression (MLR) is employed to assess the performance of the proposed technique in terms of IRI prediction accuracy. whale optimization algorithm (WOA), as a powerful metaheuristic optimization algorithm, is utilised to obtain the optimal solution to the pavement M&R optimization problem. RFR is run as an internal part of the WOA in the introduced method. Furthermore, Genetic algorithm (GA) is used to examine the performance of the proposed approach in finding the optimal solution. The RFR results conclude a more accurate prediction of IRI than MLR based on all machine learning performance indicators. Furthermore, the newly developed hybrid model significantly outperforms GA in finding the optimal and cost-effective solution to the M&R optimization problem.

关键词： whale optimization algorithm maintenance and rehabilitation random forest regression international roughness index pavement performance model pavement management system

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Interline Power Flow Controller Allocation for Active Power Losses Enhancement Using whale optimization algorithm

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ENERGIES 2024年第24期17卷 6318-6318页

作者： Alshannaq, Ahmed M. Haj-ahmed, Mohammed A. Aldwaik, Mais Abualnadi, Dia Univ Jordan Elect Engn Dept Amman 11942 Jordan Philadelphia Univ Civil Engn Dept Jarash 19392 Jordan

Transmission networks face continuous electrical and mechanical stresses due to increasing system challenges and power losses. Transmission networks require special focus and detailed studies each time a load or a generator emerges to the grid. The interline power flow controller (IPFC) is a relatively new scheme that is implemented in the transmission network to improve transmission efficiency, decrease transmission losses, and enhance voltage profile. In this paper, the interline power flow controller's impact on transmission network performance is investigated as it is implemented within the IEEE 5-bus, 14-bus, and IEEE 57-bus systems. In addition, the whale optimization algorithm (WOA) is used to optimize the interline power flow controller locations within the system to achieve optimal transmission system performance. WOA performance is also compared to genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, and the superiority of the proposed WOA-based control is proved. The robustness of the optimized system against load variations is investigated and the results introduced affirm the capability of the interline power flow controller to enhance transmission network efficiency.

关键词： interline power flow controller transmission network whale optimization algorithm

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Stacking sequence optimization of arbitrary quadrilateral laminated plates for maximum fundamental frequency by hybrid whale optimization algorithm

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COMPOSITE STRUCTURES 2023年第1期310卷

作者： Wang, Weiping Wang, Qingshan Zhong, Rui Chen, Longting Shi, Xianjie Cent South Univ State Key Lab High Performance Complex Mfg Changsha 410083 Peoples R China Cent South Univ Coll Mech & Elect Engn Changsha 410083 Peoples R China China Acad Engn Phys Inst Syst Engn Mianyang 621999 Peoples R China

A Hybrid whale optimization algorithm (HWOA) is proposed to optimize the stacking sequence of arbitrary quadrilateral composite plates. By introducing an adversarial learning strategy, a broader search is performed on the initial domain to generate a better initial population. The mutation operator and opposition-based learning (OBL) strategy of the differential evolution (DE) algorithm are combined to maintain the diversity of the evolutionary process. In addition, the nonlinear convergence factor is introduced to improve the global explo-ration ability of HWOA when dealing with discrete problems. The stacking sequences of quadrilateral composite plates with different boundary conditions and geometries are optimized to maximize the fundamental frequency. The optimized results of HWOA are compared with those of whale optimization algorithm (WOA), Aquila optimizer (AO), artificial bee colony (ABC) algorithm, DE algorithm and layerwise optimization approach (LOA) algorithm. The results show that HWOA with multiple strategy combinations has good global search capability and robustness, and thus HWOA is a good candidate optimization algorithm for composite plate optimization.

关键词： whale optimization algorithm Opposition-based learning Composite plate Free vibration Differential evolution Arbitrary quadrilateral

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The Exploration/Exploitation Tradeoff in whale optimization algorithm

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IEEE ACCESS 2019年 7卷 125919-125928页

作者： Wu, Xiaoyang Zhang, Sen Xiao, Wendong Yin, Yixin Univ Sci & Technol Beijing Sch Automat & Elect Engn Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China

The whale optimization algorithm(WOA) is a novel meta-heuristic evolutionary algorithm inspired by the behavior of whales predation. An important factor to the success of WOA is the balancing between exploration and exploitation. In the WOA, the distance control parameter a is the main factor to find an appropriate balance between exploration and exploitation. In the standard WOA, the distance control parameter a is optimized by linear control strategy (LCS), but the process of whales predation is not simply linear process. To address the issue, this paper proposed a nonlinear control strategy based on arcsine function (NCS-Arcsin) to optimize WOA. The NCS-Arcsin is applied to adjust distance control parameter a. The NCS-Arcsin is considered to accurately describe the process of whales predation. Experiments on twelve well-known benchmark functions show the NCS-Arcsin can significantly improve the exploration and exploitation capabilities of WOA. In addition, the performance of proposed NCS-Arcsin is compared with LCS and other have been proposed NCS. The experimental results show that the optimization effect of NCS-Arcsin is stronger than that of LCS and other NCS. The NCSs based on the arcsine function is the best NCS, which can significantly improve the optimize performance of WOA.

关键词： whale optimization algorithm meta-heuristic algorithm nonlinear control strategy exploration and exploitation

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Enhancing whale optimization algorithm with Levy Flight for coverage optimization in wireless sensor networks

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COMPUTERS & ELECTRICAL ENGINEERING 2021年 94卷 107359-107359页

作者： Deepa, R. Venkataraman, Revathi SRM Inst Sci & Technol Dept Comp Sci & Engn Kattankulathur Tamil Nadu India

Coverage optimization is one of the most essential pre-requisites in Wireless Sensor Networks (WSNs) which plays a significant and impactful role in the field of environmental monitoring, surveillance, socio-economic Cyber-networkings, etc. The whale optimization algorithm (WOA) is a swarm intelligence based Search-algorithm while browsing for an optimal solution, but, it suffers from the poor & inconsistent exploration problem and that causes trapping of local optima in randomly deployed nodes that fail to guarantee network coverage. To resolve the issue, an innovative study has been researched which presents an embedded coverage optimization WSN and is based on Levy Flight mechanism with WOA (LWOA) .This updates the current search of location for positioning the sensors in the field. This mechanism can enhance and balance the exploration ability of WOA, which allows trapping of the local optima. This thichnically enhanced and updated proposal LWOA is validated by 25 benchmark optimization functions and is compared with existing Particle Swarm optimization and WOA. From the experimental results, it can be construed and proved that the performance of Levy WOA (LWOA) has significantly improved the global search capacity and increase the efficiency of convergence, which immensely enhances the efficacy of coverage of nodes inturn amplifying the overall performance of the network. Finally, by using the K-Nearest Neighbour KNN(centroid) approach nearly 33% of nodes were optimized.

关键词： Coverage optimization whale optimization algorithm Levy Flights K-Nearest Neighbor (centroid)

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Hybrid whale optimization algorithm with gathering strategies for high-dimensional problems

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 179卷 115032-115032页

作者： Zhang, Xinming Wen, Shaochen Henan Normal Univ Coll Comp & Informat Engn Xinxiang Henan Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang Henan Peoples R China

In order to solve the problems, such as insufficient search ability and low search efficiency, of whale optimization algorithm (WOA) in solving high-dimensional problems, a novel Hybrid WOA with Gathering strategies (HWOAG) is proposed in this paper. Firstly, an individual-based updating way is used in HWOAG instead of the dimension-based updating one of WOA to reduce the computational complexity and to be more suitable for highdimensional problems. Secondly, a random opposition learning strategy is embedded into the individual-based WOA to form an opposition learning WOA (OWOA), and Grey Wolf Optimizer (GWO) is integrated into OWOA to form an OWOA with GWO (OWOAG) so as to improve the global search ability of WOA. Finally, two standalone OWOAGs are formulated to balance exploration and exploitation better. The two OWOAGs adopt strategies such as switching parameter tuning, random differential disturbance and global-best spiral operator to get stronger search ability. A lot of experimental results on high-dimensional (i.e. 1000-, 2000-, 4000- and 8000dimensional) benchmark functions and clustering datasets for Fuzzy C-Means (FCM) optimization show that HWOAG has stronger search ability and higher search efficiency than WOA and quite a few state-of-the-art algorithms and that all the strategies gathered to WOA are effective. The source codes of the proposed algorithm HWOAG are available at https://***/kangzhai/HWOAG.

关键词： optimization algorithm whale optimization algorithm Grey Wolf optimizer High-dimensional problems Fuzzy C-means

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Improved whale optimization algorithm Based on Hybrid Strategy and Its Application in Location Selection for Electric Vehicle Charging Stations

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ENERGIES 2022年第19期15卷 7035-7035页

作者： Li, Yongjing Pei, Wenhui Zhang, Qi Shandong Jiaotong Univ Sch Informat Sci & Elect Engn Jinan 250357 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China

The charging station location model is a nonlinear programming model with complex constraints. In order to solve the problems of weak search ability and low solution accuracy of the whale optimization algorithm (WOA) in solving location models or high-dimensional problems, this paper proposes an improved whale optimization algorithm (IWOA) based on hybrid strategies. Chaos mapping and reverse learning mechanism are introduced in the original algorithm, and the change mode of convergence factor and probability threshold is improved. Through optimization experiments on 18 benchmark functions, the test results show that IWOA has the best solution ability. Finally, IWOA is used to solve a site selection optimization model aiming at the minimum comprehensive cost. The results show that the proposed algorithm and model can effectively reduce the comprehensive cost of site selection. This provides a necessary decision-making reference for the scientific site selection for electric vehicle charging stations.

关键词： adaptive step size charging station location chaotic mapping dynamic threshold reverse learning whale optimization algorithm

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A multipopulation cooperative coevolutionary whale optimization algorithm with a two-stage orthogonal learning mechanism

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KNOWLEDGE-BASED SYSTEMS 2022年 246卷 1页

作者： Zhao, Fuqing Bao, Haizhu Wang, Ling Cao, Jie Tang, Jianxin Jonrinaldi Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 10084 Peoples R China Univ Andalas Dept Ind Engn Padang 25163 Indonesia

This paper designed a multipopulation cooperative coevolutionary framework with a two-stage orthogonal learning (OL) mechanism for the whale optimization algorithm (MCCWOA) to improve the performance of the whale optimization algorithm (WOA). In the framework, a prediction model of the neighborhood structure is established by discovering the guidance information of the following iteration process in the objective space at the first-stage OL. In the second-stage OL, an auxiliary vector pool with various features in the decision space is introduced to guide the candidates falling in the stagnant status to conduct more valuable exploration. According to the domain knowledge of the candidates, the population is divided into the elite population, the intermediate population, and the inferior population. The information of the subpopulations has interacted with the corresponding historical populations in the evolution processes to enhance the ability of cooperative coevolution among individuals. A standard set of comprehensive benchmark cases and three engineering cases are utilized to verify the advantages of the proposed algorithm. The results of the statistical analysis, diversity analysis, and convergence analysis testified that the MCCWOA outperforms the 15 state-of-the-art algorithms regarding efficiency and significance. (C) 2022 Elsevier B.V. All rights reserved.

关键词： whale optimization algorithm Multipopulation Orthogonal learning mechanism Historical information Metaheuristic

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A whale optimization algorithm-based multivariate exponential smoothing grey-holt model for electricity price forecasting

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期255卷

作者： Sapnken, Flavian Emmanuel Tazehkandgheshlagh, Ali Khalili Diboma, Benjamin Salomon Hamaidi, Mohammed Noumo, Prosper Gopdjim Wang, Yong Tamba, Jean Gaston Univ Douala Univ Inst Technol PB 8698 Douala Cameroon Univ Tehran Fac Agr Engn Tehran Iran Ziane Achour Univ Djelfa Fac Exact Sci & Comp Sci Dept Math Djelfa Algeria Univ Yaounde Fac Sci 1POB 812 Yaounde Cameroon Southwest Petr Univ Sch Sci Chengdu 610500 Sichuan Peoples R China

Accurately forecasting electricity prices is essential for a variety of stakeholders in the energy sector, including market investors, policymakers, and consumers. However, existing forecasting techniques are often limited by complex parametric estimates and strict restrictions on input variables. This paper proposes a whale optimization algorithm (WOA)-based multivariate exponential smoothing Grey-Holt (GMHES) model for electricity price forecasting. The proposed WOA-GMHES(1,N) model uses historical data to learn the underlying trends and patterns of electricity prices. The WOA algorithm is used to optimize the model parameters, which are adaptively adjusted to reflect the changing dynamics of the electricity market. The proposed model is evaluated on real high- and low-voltage electricity price data from Cameroon. The results show that the novel WOA-GMHES(1,N) model outperforms competing models, achieving RMSE and SMAPE scores of 0.1359 and 0.61%, respectively. This novel model is also computationally efficient, requiring less than 1.3 s to generate a forecast. The proposed WOA-GMHES(1,N) model is a promising novel approach for electricity price forecasting. The model is accurate, efficient, and flexible, making it a valuable tool for a variety of stakeholders in the energy sector.

关键词： Electricity price forecasting Grey -Holt model whale optimization algorithm Expert systems

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