A Talent Cultivation and Performance Evaluation Model Based on a Fuzzy Control Algorithm by Shanshan Jiang

“…The algorithm combines an adaptive neuro-Fuzzy Inference System with Adaptive Hybrid Particle Swarm Optimization. …”
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A Survey of Nature-Inspired Meta-Heuristic Algorithms in Network Alignment by Anagh Awal, Farsuram Pandit

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Fault localization for automatic train operation based on the adaptive error locating array algorithm by Yanpeng Zhang, Yuxiang Cao

“…These cases are designed to locate the MFS more easily in the given parameter range using the Adaptive Particle Swarm Optimization (APSO) algorithm. Finally, the MFS is determined. …”
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A Methodology to Characterize an Optimal Robotic Manipulator Using PSO and ML Algorithms for Selective and Site-Specific Spraying Tasks in Vineyards by Roni Azriel, Oded Degani, Avital Bechar

“…Particle Swarm Optimization (PSO) was employed as the optimization algorithm in a finite solution space, with the performance measure based on maximizing the Manipulability Index of manipulator configurations reaching all targets. …”
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Task Allocation Method for Emergency Active Debris Removal Based on the Fast Elitist Non-Dominated Sorting Genetic Algorithm by Hao Lei, Xiang Zhang, Wenhe Liao, Guoning Wei, Shuhui Fan

“…The algorithm is designed to enable the satellite swarm to execute multiple coupled tasks in succession with improved optimization efficiency. …”
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Estimating Daily Reference Crop Evapotranspiration in Northeast China Using Optimized Empirical Models Based on Heuristic Intelligence Algorithms by Zongyang Li, Zhengxin Zhao, Liwen Xing, Lu Zhao, Ningbo Cui, Huanjie Cai

“…By using the least square method (LSM) and three population heuristic intelligent algorithms—a genetic algorithm (GA), a particle swarm optimization algorithm (PSO), and a differential evolution algorithm (DE)—the parameters of eleven kinds of models were optimized, respectively, and the ETo estimation model suitable for northeast China was selected. …”
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An Enhanced Evolutionary Algorithm for Providing Energy Management Schedule in the Smart Distribution Network by Hossein Lotfi, Amir Nikooei, Ali Shojaei, Reza Ghazi, Mohammad Bagher Naghibi Sistani

“…In addition, the effect of uncertainty related to power demand is considered in the evaluations. To this end, an enhanced particle swarm optimization algorithm is provided in this paper, the proposed approach is applied to the 33-node testing system.…”
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Experimental investigation of the strength of joining dissimilar aluminum alloys by friction stir welding and optimization of effective parameters with artificial intelligence by Hosein Rostami, Saeid Nickabadi, Elyas Rostami, Kave Yazdi

“…Response surface methodology, genetic algorithms, and particle swarm optimization algorithms have been used to optimize welding parameters. …”
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Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support ve... by Yunfei Li, Dongni Zhang, Yiren Wang, Yiren Wang, Yiheng Hu, Zhongjian Wen, Zhongjian Wen, Cheng Yang, Ping Zhou, Wen-Hui Cheng

“…Based on these screened features, three models were built: Dynamic Multi-Swarm Particle Swarm Optimization SVM (DMS-PSO-SVM), Particle Swarm Optimization SVM (PSO-SVM), and a standard SVM. …”
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Peak-to-average power ratio reduction of orthogonal frequency division multiplexing signals using improved salp swarm optimization-based partial transmit sequence model by Vandana Tripathi, Prabhat Patel, Prashant Kumar Jain, Shailja Shukla

“…Therefore, an optimization algorithm, namely, the improved salp swarm optimization algorithm (ISSA), is incorpo-rated with the PTS to reduce the PAPR of the OFDM signals with limited com-putational cost. …”
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A novel economic load dispatch method of microgrid based on hybrid slime mould and genetic algorithm by Wei Ba, Wei Sun, Chunjiang Zhao, Qi Li

“…The velocity matrix is introduced to adjust the direction and speed of the individual movement to enhance the searching ability in GSMA. For performance evaluation, GSMA is compared with slime mould algorithm (SMA), grey wolf optimizer (GWO), sparrow search algorithm (SSA), Harris Hawks optimization (HHO), whale optimization algorithm (WOA) and particle swarm optimization (PSO) using standard optimization functions. …”
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Resilience-Improving Based Optimization of Post-Disaster Emergency Maintenance Strategy for Transmission Networks by Haiping LIANG, Haoyan SHI, Yan WANG, Yingpei LIU, Xinming WANG

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Constrained Fuzzy Predictive Control Using Particle Swarm Optimization by Oussama Ait Sahed, Kamel Kara, Mohamed Laid Hadjili

“…A fuzzy predictive controller using particle swarm optimization (PSO) approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. …”
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Approach of glowworm swarm optimization based virtual machine placement by Shengchao XU, Maohua XIONG, Tianqi ZHOU

“…In a cloud data center, one of the most important problems is using novel virtual machine placement strategy to promote the physical resource utilization.An approach of glowworm swarm optimization based virtual machine placement for cloud data centers called GSO-VMP was proposed.In the virtual placement, GSO algorithm was used to find a near-optimal solution.Each physical host had a luciferin value which represented the available CPU utilization.Whenever a VM was placed to a physical host, luciferin value of this physical host was updated.GSO-VMP algorithm could search the more available physical host within local range and thus the virtual migration numbers had been decreased and low energy consumption had been obtained.GSO-VMP had been evaluated using CloudSim with real-world workload data.The experimental results show that GSO-VMP has good performance in resource wastage and energy consumption.…”
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Information Diffusion Interpolation Model Based on the PSO-CV Algorithm and Its Application on Source Regions of the Yellow River by HUANG Huaping, YIN Kaixia, JIN Gaoyang

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MPPT Efficiency in PV Arrays under Partial Shading Conditions:A Comparative Analysis of PSO and P&O Algorithms by Rachid Bennia, Cherif Larbes, Faiza Belhachat

“…This investigation presents a comparative analysis of two established algorithms: Particle Swarm Optimization (PSO) and Perturb and Observe (P&O), evaluating their respective capabilities in GMPP identification. …”
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A comparative approach of single-objective optimization algorithms for energy control of flexible manufacturing system by Marius-Adrian PĂUN, Eugenia MINCĂ, Sergiu Stelian ILIESCU, Nicoleta ARGHIRA, Florin DRAGOMIR

“…The study evaluates several single-objective algorithms ‒ Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Ant Colony Optimization (ACO) to assess their potential for energy optimization in flexible manufacturing processes on FMS. …”
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Risk Assessment of Supplier R&D Investment Based on Improved BP Neural Network by Yinghua Song, Xiaoyan Sang, Zhe Wang, Hongqian Xu

“…By leveraging the ability of particle swarm optimization (PSO), whale optimization algorithm (WOA), and genetic algorithm (GA) to search for global optimal solutions, the BP neural network is improved to avoid becoming trapped in local optimal solutions and enhance the model’s generalization ability. …”
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Unlocking The Potential of Hybrid Models for Prognostic Biomarker Discovery in Oral Cancer Survival Analysis: A Retrospective Cohort Study by Leila Nezamabadi Farahani, Anoshirvan Kazemnejad, Mahlagha Afrasiabi, Leili Tapak

“…First of all, martingale residuals were obtained using a Cox regression without covariates, and were used as pseudo-survival outcome. Then, the particle swarm optimization (PSO) and genetic algorithm (GA) were used in combination with SVR for selecting features related to pseudo-survival outcome. …”
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Optimization design of an acoustic cover layer for a cylindrical cavity based on porous materials by Bohan ZHANG, Jili RONG, Xiuyan CHENG

“…In this study, a comprehensive investigation into the parameter fitting and acoustic performance optimization of porous materials is conducted, utilizing advanced physical models, multifluid impedance transfer theory, and particle swarm optimization algorithms. This research combines experimental approaches, including impedance tube experiments, with numerical methods, such as acoustic finite element analysis, to enhance the noise control capabilities of porous materials. …”
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