Optimizing Photovoltaic Panel Performance: A Comparative Study of Meta-Heuristic Algorithms by M. Sundar Rajan

“…This method was compared against other well-known algorithms such as Artificial Bee Colony (ABC), Dragonfly Algorithm (DA), Grey Wolf Optimizer (GWO), Cuckoo Search (CS), Composite Grey Wolf Optimizer (CGWO), and the standalone PSO and GWO methods. …”
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Post-Processing Optimization of the Global 30 m Land Cover Dynamic Monitoring Product by Zhehua Li, Xiao Zhang, Wendi Liu, Tingting Zhao, Weitao Ai, Jinqing Wang, Liangyun Liu

“…Post-processing optimization refers to the refinement of land cover products by applying specific rules or algorithms to minimize erroneous changes in land cover types caused by classification uncertainty or interannual phenological variations. …”
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Prediction and optimization of robot processing technology based on neural network and genetic algorithm by Fusen WU

“…Finally, the genetic algorithm was applied to optimize grinding process parameters with material removal rates as the indicator.ResultsThe grinding process parameters have significant effects on grinding forces, but the order of major and secondary effects of different parameters varies with grinding force components. …”
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A Model For Optimization of Knowledge Management Outsourcing Decision Using Genetic Algorithm by Ameneh Khadivar, Fatemeh Abbasi, Sheyda Akbarian

“…Purpose: The purpose of this study is to develop an optimization model for knowledge management outsourcing using a genetic algorithm. …”
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FPGA multi-unit parallel optimization and implementation of post-quantum cryptography CRYSTALS-Kyber by Bin LI, Xiaojie CHEN, Feng FENG, Qinglei ZHOU

“…In lattice-based post-quantum cryptography, polynomial multiplication is complicated and time-consuming.In order to improve the computational efficiency of lattice cryptography in practical applications, an FPGA multi-unit parallel optimization and implementation of post-quantum cryptography CRYSTALS-Kyber was proposed.Firstly, the flow of Kyber algorithm was described and the execution of NTT, INTT and CWM were analyzed.Secondly, the overall structure of FPGA was given, the butterfly arithmetic unit was designed by pipeline technology, and the Barrett modulus reduction and CWM scheduling optimization were used to improve the calculation efficiency.At the same time, 32 butterfly arithmetic units were executed in parallel, which shortens the overall calculation cycle.Finally, the multi-RAM channel was optimized to improve the memory access efficiency with alternate data access control and RAM resource reuse.In addition, with the loosely coupled architecture, the overall operation scheduling was realized by DMA communication.The experimental results and analysis show that the proposed scheme implemented can complete NTT, INTT and CWM operations within 44, 49, and 163 clock cycles, which is superior to other schemes and has high energy efficiency ratio.…”
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Multi-Objective Optimization of Chiral Metasurface for Sensing Based on a Distributed Algorithm by Xianglai Liao, Lili Gui, Shulei Bi, Ang Gao, Zhenming Yu, Kun Xu

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An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network by C. Vimalarani, R. Subramanian, S. N. Sivanandam

“…Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. …”
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Enhanced air quality prediction using adaptive residual Bi-LSTM with pyramid dilation and optimal weighted feature selection by R. Sudha, Ajith Damodaran, Gunaselvi Manohar

“…The proposed model introduces a novel methodology for weighted feature selection utilizing an Improved Gannet Optimization Algorithm (IGOA) aimed at enhancing the performance of data classification. …”
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Directed energy deposition (DED) of Inconel 718-SS316 L bimetallic structures: Experimental investigations and optimization through artificial humming bird algorithm for improved h... by D. Rajamani, A.R. Vinod, K. Niranjan Reddy, M. Siva Kumar

“…This study reveals the insights into the development of bimetallic structures through DED process with their microstructures, process-property relationship and multi-response optimization through metaheuristic algorithms concerning the improved high strain rate and mechanical characteristics.…”
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Kinematic Constrained RRT Algorithm with Post Waypoint Shift for the Shortest Path Planning of Wheeled Mobile Robots by Sisi Liu, Zhan Zhao, Jun Wei, Qianqian Zhou

“…Once the distance between the new node and the target is within a certain threshold, the tree growth stops and a target connection based on minimum turning radius arc is proposed to generate an initial complete random path. The most significant difference from traditional RRT-based methods is that the proposed method optimizes the path based on Dubins curves through a post waypoint shift after a random path is generated, rather than through parent node selection and rewiring during the exploring tree growth. …”
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an-QNA: An Adaptive Nesterov Quasi-Newton Acceleration-Optimized CMOS LNA for 65 nm Automotive Radar Applications by Unal Aras, Lee Sun Woo, Tahesin Samira Delwar, Abrar Siddique, Anindya Jana, Yangwon Lee, Jee-Youl Ryu

“…It includes an improved post-linearization (I_PL) technique to enhance the linearity. …”
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Innovative Hybrid Algorithm for Solving Vehicle Routing Problem with Time Window by A.M. Rahimi, B. Yadegari, M. Aboutalebi Esfahani

“…Hybrid approaches combine multiple optimization techniques to improve the quality and efficiency of solutions. …”
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Application of an Improved Minimum Noise Amplitude Deconvolution for Bearing Fault Diagnosis by Ying Ma, Siming He, Entie Qi, Yu Wei

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Healthcare Prediction Using Novel Machine Learning Methods and Metaheuristic Algorithm by Yazdan Ashgevari, Behrouz Alefy, Faranak Kazemi

“…Four models ETSA, ETCH, SVSA, and SVCH were developed in this study by integrating the ETC and SVC models with two optimization algorithms the CHS and SAO. Upon analyzing the performance of these models, it was found that the ETSA model had a high precision of 0.806 in normal conditions.…”
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Improving the Solving of Optimization Problems: A Comprehensive Review of Quantum Approaches by Deborah Volpe, Giacomo Orlandi, Giovanna Turvani

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Improving Sleep Disorder Diagnosis Through Optimized Machine Learning Approaches by Md. Atiqur Rahman, Israt Jahan, Maheen Islam, Taskeed Jabid, Md Sawkat Ali, Mohammad Rifat Ahmmad Rashid, Mohammad Manzurul Islam, Md. Hasanul Ferdaus, Md Mostofa Kamal Rasel, Mahmuda Rawnak Jahan, Shayla Sharmin, Tanzina Afroz Rimi, Atia Sanjida Talukder, Md. Mafiul Hasan Matin, M. Ameer Ali

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An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization by Jing Xiao, Jing-Jing Li, Xi-Xi Hong, Min-Mei Huang, Xiao-Min Hu, Yong Tang, Chang-Qin Huang

“…In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. …”
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A Non-Rigid Three-Dimensional Image Reconstruction Algorithm Based on Deformable Shape Reliability by Haiying Chen, Syed Atif Moqurrab

“…Most reconstruction algorithms for non-rigid three-dimensional (3D) images assume that non-rigidity can be represented as a linear combination of a fixed number of rigid bases. …”
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An Improved Large Neighborhood Search for Network-Level Airport Slot Allocation Optimization by Qiangzhe Wang, Yuanyuan Ma, Yifan Xu, Zhiyuan Shen, Dan Li

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Optimizing RetinaNet anchors using differential evolution for improved object detection by Asaad Mohammed, Hosny M. Ibrahim, Nagwa M. Omar

“…Specifically, we propose an optimization algorithm based on Differential Evolution (DE) that adjusts anchor scales and ratios while determining the most appropriate number of these parameters for each dataset based on the annotated data. …”
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