Impact of Different Mode Decomposition Methods Combined with LSTM Models on Daily Runoff Forecasting by TAN Yongjie, WANG Xianxun, DUAN Mingxu, LIU Yaru, YAO Huaming

“…A combination of modal decomposition and deep learning forecasting methods was introduced to daily runoff forecasting to address the characteristics of unstable and volatile daily runoff series.Firstly,the complete ensemble empirical modal decomposition method was used to decompose the daily runoff time series,so as to obtain the modal components of different frequency components.Secondly,the daily runoff forecasting model was constructed for different modal components based on the long short-term memory neural network (LSTM),and the hyperparameters of the forecasting model were optimized using the grid search parametric optimization algorithm.Finally,the forecasting results of each model were modally reconstructed to obtain daily runoff forecasting results.The daily runoff forecasting of the Yichang hydrological station was taken as an example.Compared with the single LSTM,the RMSE,MAE,and MAPE of the proposed combination model were reduced by 65.02%,58.35%,and 2.88%,respectively.The decomposition effect of the complete ensemble empirical mode decomposition was better than that of the traditional modal decomposition method,which provided a new method and reference for nonlinear and non-stable daily runoff forecasting in a short time scale.…”
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Dynamic Grouping Control of BESS for Remaining Useful Life Extension and Overall Energy Efficiency Improvement in Smoothing Wind Power Fluctuations by Yang Yu, Dongyang Chen, Yuwei Wu, Boxiao Wang, Yuhang Huo, Wentao Lu, Zengqiang Mi

“…Second, a model to optimize capacity allocation for three battery groups (BGs) in BESS is established considering LL and OEE, and it is solved by the designed improved beetle swarm antennae search algorithm. Then, a dynamic grouping method is proposed to dynamically adjust the grouping state of battery units (BUs) during operation to keep good sustainable dispatchability. …”
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Behavior modeling for a new flexure-based mechanism by Hunger Game Search and physics-guided artificial neural network by Hieu Giang Le, Thanh-Phong Dao, Minh Phung Dang, Thao Nguyen-Trang

“…Furthermore, applying the Hunger Game Search to a Physics-Guided Artificial Neural Network not only can reduce error but also can increase convergence speed compared to applying Hunger Game Search to conventional neural networks. …”
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Pickup and delivery problem solver for multiple mobile robots considering robot’s dynamics by Tomoaki Shimizu, Ayumu Goto, Kosuke Taneda, Takeshi Muranaka, Yuji Enoki, Toyokazu Kobayashi, Tomoya Hattori, Ryota Takamido, Jun Ota

“…The proposed algorithm was evaluated by simulation and compared to existing state-of-the-art methods such as cooperative A* and priority-based search. …”
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A hybrid genetic slime mould algorithm for parameter optimization of field-road trajectory segmentation models by Jiawen Pan, Caicong Wu, Weixin Zhai

“…This study therefore combines a genetic algorithm (GA) with a slime mould algorithm (SMA) to propose a novel enhanced hybrid algorithm (GASMA); the algorithm has superior global search capability due to the implicit parallelism of the GA, and the oscillation concentration mechanism of the SMA is used to enhance the algorithm's local search capability. …”
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Evolving Many-Objective Job Shop Scheduling Dispatching Rules via Genetic Programming With Adaptive Search Based on the Frequency of Features by Atiya Masood, Mansoor Ebrahim, Fahad Najeeb, Syed Muhammad Daniyal

“…The proposed algorithm introduces an adaptive search strategy that is implemented through re-initialization during the evolutionary process. …”
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Improved SOR signal detection algorithm in massive MIMO-TRDMA systems by Mingyue WANG, Fangwei LI, Xiaorong JING, Haibo ZHANG, Junzhou XIONG

“…In the massive multi-input multi-output time-reversal division multiple access (MIMO-TRDMA) systems, the traditional linear minimum mean square error (MMSE) algorithm achieved approximately the best performance.However, the matrix inversion of the MMSE algorithm was too complicated to ensure real-time processing of signal detection.To solve this problem, an improved successive over-relaxation (SOR) signal detection optimization algorithm was proposed.The proposed algorithm reasonably upgraded the solution of linear equations to prevent the complicated calculation of matrix inversion.Meanwhile, the steepest descent idea was used to provide an effective search direction for the SOR signal detection algorithm, achieving a rapid convergence rate and stronger inspection performance.The simulation results show that the proposed algorithm has the similar best performance with fewer update times compared with the traditional MMSE algorithm, and the calculation complexity is reduced from O(M³)to O(²).…”
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Improved SOR signal detection algorithm in massive MIMO-TRDMA systems by Mingyue WANG, Fangwei LI, Xiaorong JING, Haibo ZHANG, Junzhou XIONG

“…In the massive multi-input multi-output time-reversal division multiple access (MIMO-TRDMA) systems, the traditional linear minimum mean square error (MMSE) algorithm achieved approximately the best performance.However, the matrix inversion of the MMSE algorithm was too complicated to ensure real-time processing of signal detection.To solve this problem, an improved successive over-relaxation (SOR) signal detection optimization algorithm was proposed.The proposed algorithm reasonably upgraded the solution of linear equations to prevent the complicated calculation of matrix inversion.Meanwhile, the steepest descent idea was used to provide an effective search direction for the SOR signal detection algorithm, achieving a rapid convergence rate and stronger inspection performance.The simulation results show that the proposed algorithm has the similar best performance with fewer update times compared with the traditional MMSE algorithm, and the calculation complexity is reduced from O(M³)to O(²).…”
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Realization and discussion of selected artificial intelligence algorithms in computer games by Yurii Tyshchenko

“… The study explores the usage of reinforcement learning algorithms in computer card games, such as Proximal Policy Optimization and Monte Carlo Tree Search. …”
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A Bi-Objective Optimization Strategy of a Distribution Network Including a Distributed Energy System Using Stepper Search by Suliang Ma, Zeqing Meng, Yilin Cui, Guanglin Sha

“…Therefore, a stepper search optimization (SSO) method has been proposed for a bi-objective optimization problem (BiOP). …”
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Clients Purchasing Tendency Community Classification in E-Commerce Scenario: Multi-Feature Search for Densely Distributed Clients in Huge Network by Mingyuan Li, Chun-Ming Yang, Yi-Wei Kao

“…This network maps the relational landscape of users with comparable tastes. A dedicated community search algorithm uncovers densely interconnected subgroups. …”
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Comprehensive Adaptive Enterprise Optimization Algorithm and Its Engineering Applications by Shuxin Wang, Yejun Zheng, Li Cao, Mengji Xiong

“…These results are much better than those achieved by the traditional ED algorithm and the other comparative algorithms. Overall, through the coordinated implementation of multiple optimization strategies, the CAED algorithm exhibits high precision, strong robustness, and rapid convergence when searching in complex solution spaces. …”
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THE MODIFIED MARINE PREDATOR ALGORITHM FOR SOLVING OPTIMIZATION PROBLEMS (MMPA) by Nisreen Al Barrak, Hegazy Zaher, Naglaa Ragaa Saeid, Eman Oun

“…It is integrated into the transaction phase of the algorithm to guide search agents toward optimal solutions adaptively. …”
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Parameter Extraction for Photovoltaic Models with Flood-Algorithm-Based Optimization by Yacine Bouali, Basem Alamri

“…The FLA’s performance is systematically evaluated against nine recently developed optimization algorithms through comprehensive comparative and statistical analyses. …”
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Memetic Salp Swarm Algorithm for economic load dispatch problems by Mohammed A. Awadallah, Mohammed Azmi Al-Betar, Malik Braik, Raed Abu Zitar, Khaled Assaleh, Mahmud Alkoffash, Qusai Yousef Shambour

“…A six-unit generator with a capacity of 1,263 MW (6 UG-1,263 MW) and a fifteen-unit generator with a capacity of 2,630 MW (40 UG-2,630 MW) are two real-world cases discussed. Compared with other existing algorithms, the comparative results demonstrate the feasibility and usefulness of the proposed MSSA algorithm.…”
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An enhanced seasons optimization algorithm for numerical optimization and engineering design by Hojjat Emami, Mojtaba Fardi, Babak Azarnavid

“…Abstract This paper introduces an Enhanced Seasons Optimization (ESO) algorithm that significantly enhances the search performance and solution quality of the standard Seasons Optimization (SO) algorithm. …”
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A Novel Co-Evolutionary Multi-Objective Optimization Algorithm by ZHU Haifeng

“…Through simulation experiments, the results showed that the proposed algorithm improved convergence and distribution indicators by at least 84% and 76% respectively compared to NSGA-II, a classic multi-objective evolutionary algorithm, underscoring its superior search performance.…”
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A Multiobjective Brain Storm Optimization Algorithm Based on Decomposition by Cai Dai, Xiujuan Lei

“…The experimental results show that MBSO/D is more efficient than compared algorithms and can improve the search efficiency for most test problems.…”
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Path Planning of Surface vessel based on improved IRRT* algorithm by Hao Yu, Xin Wang, Hao Peng

“…Simulation experiments and actual sea area tests show that compared with the existing algorithms, the average Computation time of the improved algorithm is reduced by 71 %, 69 % and 79 % respectively, and the planned path length is reduced by 28 %, 28 % and 1 %, respectively. the number of search nodes is reduced by 88 %, 86 % and 81 %. …”
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Influence Maximization in Social Networks Using Improved Genetic Algorithm by Ali Chodari Khosroshahi, Saeid Taghavi Afshord, Bagher Zarei, Bahman Arasteh

“…By utilizing the proposed local search, the search space is efficiently explored, leading to an increase in the convergence speed of the algorithm. …”
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