Research on the A* Algorithm Based on Adaptive Weights and Heuristic Reward Values by Xizheng Wang, Gang Li, Zijian Bian

“…Secondly, a radial basis function is used to act as the adaptive weighting coefficient of the heuristic function and adjust the proportion of heuristic functions in the algorithm accordingly to the search distance. Again, optimize the cost function using the reward value provided by the target point so that the current point is away from the local optimum. …”
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Optimized Allocation of Flood Control Emergency Materials Based on Loss Quantification by Wei Wang, Yunqing Wang, Li Huang, Yue Song

“…Compared with the initial allocation, the optimized scheme reduces out‐of‐stock losses by approximately $392,000, lowers transportation costs by over $110,000, and improves the efficiency of flood control emergency scheduling, which can help management make better decisions on the allocation of flood control emergency materials in the future.…”
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Multi-objective particle swarm optimization algorithm-based method for optimal placement and sizing of distributed generations and shunt capacitors in a radial distribution network by Pinank Patel, Nagaraj Patil, Ahmed Mohsen, Aditya Kashyap, Nofal Adrees Hasan, Karthikeyan A, Dhirendra Nath Thatoi, Deepak Gupta, Alireza kamranfar

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Complementary Filter Optimal Tuning Methodology for Low-Cost Attitude and Heading Reference Systems with Statistical Analysis of Output Signal by Grzegorz Kopecki, Zbigniew A. Łagodowski

“…A simple method for acquiring calibration data is introduced, and these data are subsequently used in the proposed iterative algorithm for optimal time constant selection. The described method minimizes measurement errors and improves the accuracy of the system, ensuring operational stability. …”
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Adaptive energy loss optimization in distributed networks using reinforcement learning-enhanced crow search algorithm by S. Bharath, A. Vasuki

“…Unlike traditional methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and standard Crow Search Algorithm (CSA), which suffer from premature convergence and limited adaptability to real-time variations, Reinforcement Learning Enhanced Crow Search Algorithm (RL-CSA) which is proposed in this research work solves network reconfiguration optimization problem and minimize energy losses. …”
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Resilient VPP cost optimization in DER-driven microgrids for large distribution systems considering uncertainty during extreme events by T.D. Suresh, M. Thirumalai, R. Hemalatha, Mohit Bajaj, Vojtech Blazek, Lukas Prokop

“…Utilizing a modified IEEE 118-bus radial distribution system (RDS), segmented into residential, commercial, and industrial zones, the black widow optimization (BWO) algorithm is employed to optimally size and site VPPs, minimizing operational costs and maximizing system resilience. …”
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Parameter Optimization of Milling Process for Surface Roughness Constraints by GUO Bin, YUE Caixu, ZHANG Anshan, JIANG Zhipeng, YUE Daxun, QIN Yiyuan

“… In the milling process of 6061 aluminum considering the requirement of controlling the surface roughness of workpiece, artificially selected milling parameters may be conservative, resulting in low material removal rate and high manufacturing cost.Taking the surface roughness as the constraint condition and the maximum material removal rate as the goal, the surface roughness regression model is established based on extreme gradient boosting (XGBOOST) with the spindle speed, feed speed and cutting depth as the optimization objects.The milling parameters of spindle speed, feed speed and cutting depth are optimized by genetic algorithm.The optimal milling parameters are obtained by using the multi objective optimization characteristics of genetic algorithm.It can be seen from the four groups of optimization results that the maximum change of surface roughness is only 0.048μm, while the minimum material removal rate increases by 2458.048mm3/min.While achieving surface roughness, the processing efficiency is improved, and the manufacturing costs are reduced, resulting in good optimization effects, which has a certain guiding role in the actual processing.…”
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Prediction of Interest Rate Using Artificial Neural Network and Novel Meta-Heuristic Algorithms by Milad Shahvaroughi Farahani

“…The main goal of this article, as it is clear from the title, is the prediction of interest rate using ANN and improving the network using some novel heuristic algorithms such as Moth Flame Optimization algorithm (MFO), Chimp Optimization Algorithm (CHOA), Time-varying Correlation Particle Swarm Optimization algorithm (TVAC-PSO), etc. we used 17 variables such as oil price, gold coin price, house price, etc. as input variables. …”
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Application of Artificial Intelligence with Ant Colony Algorithm in construction projects schedule by Kiana Ahghari

“…So useMethodology of Scheduling of Projects with Artificial Intelligence and with the Approach of Ant Colony Algorithm for Organizations MethodOptimal and practical among other methods. …”
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LM-CNN-based Automatic Cost Calculation Model for Power Transmission and Transformation Projects by Xiaolin WU, Ling LUAN, Lianwu PAN, Hailong LI

“…Compared with the BP neural network and GD-CNN, the proposed model with higher prediction accuracy and stability combines the advantages of Levenberg-Marquart algorithm and convolutional neural network model to improve the calculation effect of power transmission and transformation project cost.…”
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Three-Dimensional Path Planning for Unmanned Aerial Vehicles Based on Hybrid Multi-Strategy Dung Beetle Optimization Algorithm by Hongmei Fei, Ruru Liu, Leilei Dong, Zhaohui Du, Xuening Liu, Tao Luo, Jie Zhou

“…This paper proposes a novel UAV path planning method based on the Hybrid Multi-Strategy Dung Beetle Optimization Algorithm (HMSDBO), which effectively reduces path length and improves path smoothness. …”
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Two-stage robust planning for wind power-photovoltaic-thermal power-pumped storage-battery hybrid system by LUO Yuanxiang, FAN Lidong, WANG Yuhang, LIU Cheng, JIAO Yinghe, WANG Yunlong

“…In the first stage, the capacity configuration of the hybrid system is aiming at minimizing the sum of investment cost and operation and maintenance cost. In the second stage, under a given capacity configuration, the optimal scheduling scheme is determined by constructing an uncertain set of wind power-photovoltaic output, aiming at minimizing the sum of environmental cost and cost of wind power-photovoltaic abandonment. …”
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Optimization method improvement for nonlinear constrained single objective system without mathematical models by HOU Gong-yu, XU Zhe-dong, LIU Xin, NIU Xiao-tong, WANG Qing-le

“…In addition, samples are needed to solve such system optimization problems. Therefore, to improve the optimization accuracy of nonlinear constrained single objective systems that are without accurate mathematical models while considering the cost of obtaining samples, a new method based on a combination of support vector machine and immune particle swarm optimization algorithm (SVM-IPSO) is proposed. …”
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FedDBO: A Novel Federated Learning Approach for Communication Cost and Data Heterogeneity Using Dung Beetle Optimizer by Dongyan Wang, Limin Chen, Xiaotong Lu, Yidi Wang, Yue Shen, Jingjing Xu

“…However, it also faces challenges such as high communication costs and heterogeneous client data.In order to address the above issues. …”
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An Enhanced Forwarding Method based on Intelligent Water Drops Algorithm in Named Data Network by HamidReza Afzal, Behrang Barekatain, Zahra Beheshti

“…Although Named Data Network (NDN) has made a bright future in Internet for high volume of requests by many users, how to send a request package (I-Pkt) consciously from the consumer to the Producer and returning the data package (D-Pkt) inversely is still one of its most important challenges. According to the recent limited researches, using service quality parameters beside an optimization algorithm like ant colony to find the optimal path has been an appropriate response to solve this problem. …”
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Machine Learning-Assisted Optimization of Femtosecond Laser-Induced Superhydrophobic Microstructure Processing by Lifei Wang, Yucheng Gu, Xiaoqing Tian, Jun Wang, Yan Jia, Junjie Xu, Zhen Zhang, Shiying Liu, Shuo Liu

“…To address this issue, a process optimization framework that couples machine learning and genetic algorithms was proposed and successfully applied to the optimization of femtosecond laser-induced groove structures on TC4 alloy surfaces. …”
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Reflective Distributed Denial of Service Detection: A Novel Model Utilizing Binary Particle Swarm Optimization—Simulated Annealing for Feature Selection and Gray Wolf Optimization-... by Daoqi Han, Honghui Li, Xueliang Fu

“…The BPSO-SA algorithm enhances the global search capability of Particle Swarm Optimization (PSO) using the SA mechanism and effectively screens out the optimal feature subset; the GWO algorithm optimizes the hyperparameters of LightGBM by simulating the group hunting behavior of gray wolves to enhance the detection performance of the model. …”
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Optimization of Graphene Oxide’s Characteristics with TOPSIS Using an Automated Decision-Making Process by Javanbakht T.

“…Moreover, their advantages and inconveniences could be investigated better once this investigation provides information on optimizing its candidates. In the current research work, a novel automated decision-making process was used with the TOPSIS algorithm using the Łukasiewicz disjunction, which helped detect the confusion of properties and determine its impact on the rank of candidates. …”
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Multiobjective Demand Double-Layer Energy Consumption Optimization Strategy for Microgrid Based on Improved HPSOFA by Bin Zhang, Jue Wang, Bo Li

“…In order to optimize the economy and environmental protection of microgrid, this paper establishes a demand response model based on comprehensive satisfaction, combines the advantages of the classical multiobjective particle swarm algorithm and multiobjective firefly algorithm, and proposes a hybrid particle swarm optimization and firefly algorithm (HPSOFA) to solve the joint economic and environmental dispatch problem of microgrid and improve the wind and light consumption capacity. …”
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Optimizing Assembly Error Reduction in Wind Turbine Gearboxes Using Parallel Assembly Sequence Planning and Hybrid Particle Swarm-Bacteria Foraging Optimization Algorithm by Sydney Mutale, Yong Wang, De Tian

“…The methodology results in a 38% reduction in total assembly errors, improving both process accuracy and efficiency. Specifically, the PSBFO algorithm reduced errors from an initial value of 50 to a final value of 5 across 20 iterations, with components such as the low-speed shaft and planetary gear system showing the most substantial reductions. …”
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