Optimal power scheduling in real-time distribution systems using crow search algorithm for enhanced microgrid performance by Ganesh Selvaraj, Kanimozhi Rajangam, Pradeep Vishnuram, Mohit Bajaj, Ievgen Zaitsev

“…The main contributions of this work include: (1) developing a new meta-heuristic approach for power scheduling in microgrids using the crow search algorithm, (2) achieving optimal power flow and load scheduling to minimize TOC and improve VR, and (3) successfully implementing the proposed methodology in a real-time distribution system using ETAP. …”
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A Robust Seamline Extraction Method for Large-Scale Orthoimages Using an Adaptive Cost A* Algorithm by Zhonghua Hong, Zihao Zhang, Shangcheng Hu, Ruyan Zhou, Haiyan Pan, Shijie Liu, Qing Fu, Xiaohua Tong

“…Then, an improved A* algorithm is employed to optimize the seamline, utilizing an adaptive obstacle threshold and structural similarity index to construct the heuristic function. …”
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Evolutionary Cost Analysis and Computational Intelligence for Energy Efficiency in Internet of Things-Enabled Smart Cities: Multi-Sensor Data Fusion and Resilience to Link and Devi... by Khalid A. Darabkh, Muna Al-Akhras

“…When compared to the most recent and relevant protocols, including the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), our approach achieves very promising results. …”
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Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm by Nasiru Muhammad Dankolo Dankolo, Nor Haizan Mohamed Radzi, Noorfa Haszlinna Mustaffa, Mohd Shukor Talib, Zuriahati Mohd Yunos, Danlami Gabi

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Coal Price Forecasting Using CEEMDAN Decomposition and IFOA-Optimized LSTM Model by Zhuang Liu, Xiaotuan Li

“…Abstract This study introduces a novel hybrid forecasting model for coking coal prices, integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long short-term memory (LSTM) neural networks, enhanced by an improved fruit fly optimization algorithm (IFOA). The approach begins with CEEMDAN decomposing the coking coal price sequence into intrinsic mode functions (IMFs) and a residual component, effectively mitigating non-stationarity and nonlinearity. …”
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Coordinated optimal scheduling of island microgrid for power-hydrogen-carbon integration based on SAO-NSGA-II algorithm by XI Honglei, SUN Jingliao, QU Hezuo, SHI Zhengchai, HU Changhong, LIU Jinyuan

“…Finally， through simulation examples， a comparative analysis of the results before and after the algorithm improvement is performed， validating the feasibility of the proposed improved algorithm and optimal scheduling model. …”
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Optimized parameter estimation of lithium-ion batteries using an improved cuckoo search algorithm under variable temperature profile by Tasadeek Hassan Dar, Satyavir Singh

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Application of Improved Electromagnetism-like Mechanism Algorithm on Massive Remote Sensing Image Screening by Yingjie Fang, Kun Liu, Xun Zhang, Gang Lin, Huibing Wang, Lina Dong, Su Li

“…Compared with the greedy algorithm, the optimal solution of the improved algorithm is increased by about 9.78% on average. …”
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A Bi-Level Capacity Optimization Method for Hybrid Energy Storage Systems Combining the IBWO and MVMD Algorithms by Qiaoqiao Xing, Shidong Li, Da Qiu, Yang Long, Qinyi Liao, Xiangjin Yin, Yunxiang Li, Kai Qian

“…The outer layer minimizes the annual total cost through the application of an Improved Beluga Whale Optimization (IBWO) algorithm, which is enhanced by strategies including the reverse elitism strategy, horizontal and vertical crossover operations, and a whirlwind scavenging strategy to improve performance. …”
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Improving the Analytical Method of Convergence Using Runge-Kutta Optimization Algorithm for Solving Fractional-Order Nonlinear Differential Equations by Shwan Abdal, Kais Ibraheem

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Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer by Mohd Herwan Sulaiman, Zuriani Mustaffa

“…Results demonstrate that the CNN-LSTM-BMO achieves superior performance with the lowest Root Mean Square Error (RMSE) of 0.5523 and highest R² value of 0.9435, showing statistically significant improvements over other optimization methods as confirmed by paired t-tests (P < 0.05). …”
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Site Selection Optimization Strategy for Independent Secondary Device Integrated Center Based on Improved Artificial Bee Colony Algorithm by Xiaoping XIONG, Shaolei HAO, Di SUN, Guangyang LIN, Hang LI

“…Firstly, the possible interval of the optimal solution is calculated with the improved artificial bee colony algorithm; and then the ISDIC site node that is most suitable for the center is obtained through precise calculations; and finally the practical engineering significance of the model and the superiority of the method are fully illustrated through analyzing the indexes of the optimal node.…”
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Multi-Objective Scheduling Optimization of Prefabricated Components Production Using Improved Non-Dominated Sorting Generic Algorithm II by Yishi Zhao, Shaokang Du, Ming Tu, Haichuan Ma, Jianga Shang, Xiuqiao Xiang

“…Given the complex modeling and numerous constraints faced by the production of prefabricated components, we propose an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. …”
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Optimization of electric vehicle charging facility layout considering the enhancement of renewable energy consumption capacity and improvement of PSO algorithm by Di Zheng, Baobao Zheng

“…These three improvements include randomly updating inertia weights, introducing acceleration factors to replace learning factors, and introducing fast non-dominated sorting for better or worse selection, and improving the optimization ability of the algorithm by solving the crowding distance. …”
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Energy storage configuration considering user-shared costs in peak shaving auxiliary services with improved multi-objective particle swarm optimization by Yiyou Xing, Jin Shen, Xinru Li

“…Moreover, an improved particle swarm optimization algorithm, specifically adapted for this model, is developed. …”
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Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms by Fude Duan, Mahdiyeh Eslami, Mohammad Khajehzadeh, Ali Basem, Dheyaa J. Jasim, Sivaprakasam Palani

“…The variables are microgrid optimal location and capacity of the HMG components in the network which are determined through a multi-objective improved Kepler optimization algorithm (MOIKOA) modeled by Kepler’s laws of planetary motion, piecewise linear chaotic map and using the FDMT. …”
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Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA by Yang Sen, Wang Hengdi, Cui Yongcun, Li Chang, Tang Yuanchao

“…Secondly, the improved SSA optimizes the important parameters (decomposition number <italic>K</italic> and penalty factor <italic>α</italic>) of the VMD algorithm, and the fittness function adopts the minimum envelope entropy. …”
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Multi-objective Optimization Configuration of Capacitor Blocking Devices Based on Field-Circuit Coupled Model and Improved Differential Evolution Algorithm by Hongwei LIU

“…Based on the operating characteristics of the DC blocking devices, this paper analyzes the influence of the DC blocking devices on the DC current near the neutral point of the transformers, and proposes a multi-objective optimization configuration method for capacitor blocking devices based on the field-circuit coupling model and improved differential evolution algorithm. …”
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The Task of Allocating Operations to Work Centers: Modern Approaches to Cost Minimization and Efficiency Improvement under Resource Constraints by Nikolay Nikitin, Igor Ilin, Alissa Dubgorn

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PSO Based Optimization of Testing and Maintenance Cost in NPPs by Qiang Chou, Daochuan Ge, Ruoxing Zhang

“…In this paper, we adopt PSO as an optimizer to optimize the multiobjective optimization problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. …”
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