Optimal Configuration of Electricity-Hydrogen Hybrid Energy Storage System Based on Multi-objective Artificial Hummingbird Algorithm by Zijing LU, Zishou LI, Xiangguo GUO, Bo YANG

“…The multi-objective artificial hummingbird algorithm based on Pareto is used to solve the planning scheme and then compared with the multi-objective particle swarm optimization and multi-objective atomic orbital search algorithm. …”
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Optimizing FACTS Device Placement Using the Fata Morgana Algorithm: A Cost and Power Loss Minimization Approach in Uncertain Load Scenario-Based Systems by Mohammad Aljaidi, Pradeep Jangir, Sunilkumar P. Agrawal, Sundaram B. Pandya, Anil Parmar, Ali Fayez Alkoradees, Arpita, Aseel Smerat

“…The FATA algorithm is evaluated against recently developed and improved optimization techniques, such as rime-ice formation phenomenon based Improved RIME (IRIME) Algorithm, Newton–Raphson-Based Optimization (NRBO), Resistance Capacitance Algorithm (RCA), Krill Optimization Algorithm (KOA), and Grey Wolf Optimizer (GWO), across multiple optimization objectives: reduction in generation cost, reduction in power loss and combined generation cost plus power loss, termed as Gross cost function. …”
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FHQ-RRT*: An Improved Path Planning Algorithm for Mobile Robots to Acquire High-Quality Paths Faster by Xingxiang Dong, Yujun Wang, Can Fang, Kemeng Ran, Guohui Liu

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Pipeline corrosion rate prediction model using BP neural network based on improved sparrow search algorithm by Shuhui XIAO, Chuanjia DU, Chengjun WANG

“…Methods This paper proposes a pipeline corrosion rate prediction model using an optimized BP neural network based on an improved Sparrow Search Algorithm to address the aforementioned disadvantages. …”
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Low Carbon Economic Dispatch of Power System Based on Multi-Region Distributed Multi-Gradient Whale Optimization Algorithm by Linfei Yin, Yongzi Ye, Xiaoping Xiong, Jiajia Chai, Hanzhong Cui, Haoyuan Li

“…In this study, MRDMGWOA is simulated on the IEEE 39 system and 118 system, and its performance is compared with other heuristic algorithms. The results show that: (1) in the IEEE 39 system, MRDMGWOA reduces the power generation cost and CO₂ emission by 17% and 22%, respectively, and reduces the computation time by 16.14 s compared with the centralized optimization; (2) in the IEEE 118 system, the two metrics are further optimized, with a 20% and 17% reduction in the cost and emission, respectively, and an improvement in the computational efficiency by 45.46 s; (3) in the spacing, hypervolume, and Euclidian metrics evaluation, MRDMGWOA outperforms other algorithms; (4) compared with the existing DMOGWO and DMOMFO, the computation time of MRDMGWOA is reduced by 177.49 s and 124.15 s, respectively, and the scheduling scheme obtained by MRDMGWOA is more optimal than DMOGWO and DMOMFO.…”
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Novel Gaussian-Decrement-Based Particle Swarm Optimization with Time-Varying Parameters for Economic Dispatch in Renewable-Integrated Microgrids by Yuan Wang, Wangjia Lu, Wenjun Du, Changyin Dong

“…Background: To address the uncertainties of renewable energy power generation, the disorderly charging characteristics of electric vehicles, and the high electricity cost of the power grid in expressway service areas, a method of economic dispatch optimization based on the improved particle swarm optimization algorithm is proposed in this study. …”
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Two-layer optimization model of distribution network line loss considering the uncertainty of new energy access by Xiping Ma, Xiping Ma, Xiaoyang Dong, Haitao Xiao, Yaxin Li, Rui Xu, Kai Wei, Juanjuan Cai, Juan Wei

“…Both layers are solved using the Improved Whale Optimization algorithm (IWOA). Then, the IEEE-33 node distribution system was taken as a simulation example to verify the effectiveness and superiority of the proposed model and algorithm.…”
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An effectiveness of machine learning models for estimate the financial cost of assistive services to disability care in the Kingdom of Saudi Arabia by Obaid Algahtani, Mohammed M. A. Almazah, Farouq Alshormani

“…Eventually, the modified pelican optimization algorithm (MPOA) is utilized to fine-tune the optimal hyperparameter of ensemble model parameters to achieve high predictive performance. …”
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A Low-Carbon Scheduling Method for Container Intermodal Transport Using an Improved Grey Wolf–Harris Hawks Hybrid Algorithm by Meixian Jiang, Shuying Lv, Yuqiu Zhang, Fan Wu, Zhi Pei, Guanghua Wu

“…The model is solved using an improved grey wolf–Harris hawks hybrid algorithm (IGWOHHO). …”
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TOPSIS inspired cost-efficient concurrent workflow scheduling algorithm in cloud by K. Kalyan Chakravarthi, L. Shyamala, V. Vaidehi

“…Therefore, it is a great challenge to improve system performance and optimize several scheduling criteria simultaneously. …”
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Ship path planning based on improved multi-scale A* algorithm of collision risk function by Chunyu Song, Teer Guo, Jianghua Sui

“…Moreover, in order to improve the efficiency of optimal path planning in the context of complex sea areas while ensuring path safety, the paper proposes a collision risk function to optimize the determination of the cost of A* algorithm nodes, thereby enhancing the heuristic function of the A* algorithm. …”
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Tuning Genetic Algorithm Parameters to Improve Convergence Time by Maria Angelova, Tania Pencheva

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Time-Dependent Multi-Center Semi-Open Heterogeneous Fleet Path Optimization and Charging Strategy by Tingxin Wen, Haoting Meng

“…The self-organizing mapping network method is employed to initialize the EV routing, and an improved adaptive large neighborhood search (IALNS) algorithm is developed to solve the optimization problem. …”
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Fast-Charging Optimization Method for Lithium-Ion Battery Packs Based on Deep Deterministic Policy Gradient Algorithm by Zhi Zhang, Taijun Guo, Yefeng Liu, Xinfu Pang, Zedong Zheng

“…First, a deep reinforcement learning charging optimization model is constructed, aiming to minimize charging time and SOC balancing cost, with constraints on battery voltage, temperature, SOC, and SOH. …”
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Research on Day-Ahead Optimal Scheduling of Wind–PV–Thermal–Pumped Storage Based on the Improved Multi-Objective Jellyfish Search Algorithm by Yunfei Hu, Kefei Zhang, Sheng Liu, Zhong Wang

“…It is assessed using the improved multi-objective jellyfish search (IMOJS) algorithm, and its effectiveness is demonstrated through comparison with a fixed-speed pumped storage (FS-PS) system. …”
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Dynamic parking space allocation at urban scale: Problem formulation and resolution by Hanae Errousso, Jihane El Ouadi, El Arbi Abdellaoui Alaoui, Siham Benhadou

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Optimizing Rural MG’s Performance: A Scenario-Based Approach Using an Improved Multi-Objective Crow Search Algorithm Considering Uncertainty by Mohammad Hossein Taabodi, Taher Niknam, Seyed Mohammad Sharifhosseini, Habib Asadi Aghajari, Seyyed Mohammad Bornapour, Ehsan Sheybani, Giti Javidi

“…A new optimization method, namely the Improved Multi-Objective Crow Search Algorithm (IMOCSA), is proposed to solve the problem models. …”
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Two-Layer Optimal Scheduling and Economic Analysis of Composite Energy Storage with Thermal Power Deep Regulation Considering Uncertainty of Source and Load by Chao Xing, Jiajie Xiao, Xinze Xi, Jingtao Li, Peiqiang Li, Shipeng Zhang

“…The upper layer takes pumped storage as the optimization goal to improve net load fluctuation and the optimal peak load benefit; the lower layer takes the system’s total peak load cost as the optimization goal and obtains a day-before scheduling plan for the energy storage system, using an improved gray wolf algorithm to process it. …”
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Determining Appropriate Strategy for Managing the Energy Production System in the Presence of Renewable Energy Sources and Improved Intelligence Method by Amir Sohel, Md Faisal Bin Ashab

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An optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero‐carbon DC microgrid by Ying Han, Yujing Hou, Luoyi Li, Weifeng Meng, Qi Li, Weirong Chen

“…This paper provides an optimal control method based on the mutual learn salp swarm algorithm (MLSSA) in real‐time, which aims to enhance the economy and extend the system's service life. …”
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