Optimizing a Hybrid Controller for Automotive Active Suspension System by Using Genetic Algorithms With Two High Level Parameters by Vu Van Tan, Do Trong Tu, Nguyen Van Vinh, Pham Tat Thang, Andras Mihaly, Peter Gaspar

“…Subsequently, the coefficients of the HASS are optimized through the Genetic Algorithm optimization with the parameter <inline-formula> <tex-math notation="LaTeX">$\beta =0.1,\,0.5,\,0.9$ </tex-math></inline-formula>, aiming to enhance ride comfort and road holding corresponding to each value of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>. …”
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Optimization of Francis turbine runner based on analytic hierarchy process – entropy weight method and multi-objective Lichtenberg algorithm by Xiaobo Zheng, Wei Wang, Yaping Zhao, Hao Liang, Pengcheng Guo, Zhihua Li

“…To address these issues, this study develops a multi-condition, multi-objective optimization platform for a Francis turbine runner based on the Analytic Hierarchy Process-Entropy Weight (AHP) method and the Multi-Objective Lichtenberg Algorithm (MOLA). …”
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The quick crisscross sine cosine algorithm for optimal FACTS placement in uncertain wind integrated scenario based power systems by Sunilkumar P. Agrawal, Pradeep Jangir, Laith Abualigah, Sundaram B. Pandya, Anil Parmar, Absalom E. Ezugwu, Arpita, Aseel Smerat

“…The Quick Crisscross Sine Cosine Algorithm (QCSCA) was developed to address the challenges of solving the Optimal Power Flow (OPF) problem in power systems that integrate renewable energy sources and Flexible AC Transmission Systems (FACTS) devices. …”
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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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Hyperparameter Optimization for Problem-Based Custom CNN Architectures Using a Smart Grid Search Method by H. Aktas

“…The proposed method was also compared with Bayesian optimization using a similar search space. While Bayesian optimization achieved the same highest test accuracy (99.22%), it produced a model that was 3.7 times larger. …”
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A classifier-assisted evolutionary algorithm with knowledge transfer for expensive multitasking problems by Min Hu, Zhigang Ren, Zhirui Cao, Yifeng Guo, Haitao Sun, Hongyao Zhou, Yu Guo

“…Abstract Surrogate-assisted evolutionary algorithms provide an effective means for complex and computationally expensive optimization problems. …”
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Solving the Economic Load Dispatch Problem by Attaining and Refining Knowledge-Based Optimization by Pravesh Kumar, Musrrat Ali

“…The Static Economic Load Dispatch (SELD) problem is a paramount optimization challenge in power engineering that seeks to optimize the allocation of power between generating units to meet imposed constraints while minimizing energy requirements. …”
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Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm by Aseel Bennagi, Obaida AlHousrya, Daniel T. Cotfas, Petru A. Cotfas

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Adaptive Ruminant Optimization With LoRa-Based Communication for Formation Control of Multiple UAVs by Muhammad Aamir Khan, Zain Anwar Ali, Muhammad Haris Muneer, Raza Hasan

“…The first scenario is designed to test the designed algorithm and hence contains no obstacles. The second scenario introduces some obstacles in the form of mountains to see whether the proposed algorithm can avoid the obstacles while maintaining the formation. …”
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Smart village construction and college students’ skills entrepreneurship path based on LSTM and simulated annealing algorithm by Xueli Dong

“…In addition, the LSTM is suitable for processing time series data and can effectively analyze the development and changes of smart rural construction, while the simulated annealing algorithm can efficiently find a near-optimal solution to optimize the entrepreneurial path. …”
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Optimization of Fusion Method for GF-2 Satellite Remote Sensing Images based on the Classification Effect by Jintong Ren, Wunian Yang, Xin Yang, Xiaoyu Deng, He Zhao, Fang Wang, Lei Wang

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Multi-objective optimization and parameter sensitivity study on microreactor nuclear power systems by Ersheng You, Yiyi Li, Jianjun Xu, Dianchuan Xing, Haochun Zhang

“…The PSO algorithm code was used to calculate and compare the benchmark case of MRNPS, and the optimized Pareto optimal solution set was obtained. …”
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A Ultra-Low Cost and Accurate AMC Algorithm and Its Hardware Implementation by Yuqin Zhao, Tiantai Deng, Bill Gavin, Edward A. Ball, Luke Seed

“…In this design, the CAMC algorithm is optimized to fit the FPGA characteristics to further improve the performance, and the computing demands of which could be saved over 94&#x0025; compared with other state-of-the-art designs. …”
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A Comparison of Heuristic Algorithms for Solving the Traveling Salesman Problem by Younes Khdeir, Ahmed Awad

“…The study examines how these algorithms can solve the TSP and avoid local minimum values while achieving a balance between research exploration and exploitation for an optimal solution. …”
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Enhanced energy-efficient cluster-based routing with spotted hyena optimization in heterogeneous WSNs by Ved Prakash, Suman Pandey, Biswa Mohan Sahoo

“…Abstract Wireless sensor networks (WSNs) are crucial in many applications; hence, it is important to have routing algorithms that are energy-efficient in order to extend the lifespan of the network. …”
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Adaptive Sharding for UAV Networks: A Deep Reinforcement Learning Approach to Blockchain Optimization by Kaiyin Lu, Xinguang Zhang, Tianbo Zhai, Mengjie Zhou

“…The proposed optimizations focus on dual objectives: enhancing data security while concurrently accelerating processing speeds. …”
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Flexible Reconfiguration for Optimal Operation of Distribution Network Under Renewable Generation and Load Uncertainty by Behzad Esmaeilnezhad, Hossein Amini, Reza Noroozian, Saeid Jalilzadeh

“…The uncertainty of the load and generation from renewable energies is planned to use their probability density functions via a scenario-based approach. The suggested optimization problem is solved using a metaheuristic approach based on the coati optimization algorithm (COA) due to the nonlinearity and non-convexity of the problem. …”
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Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II by Yujing Liu, Chunmei Chen, Yu Sun, Shaojie Miao

“…In multi-UAV collaborative power grid inspection, the system efficiency of existing methods is limited by the performance of both task assignment and path planning, which is critical in large-scale task scenarios, resulting in a huge computational cost and a high possibility to local optimality. To address these challenges, a bilevel optimization framework based on GA-NSGA-II and task segmentation is proposed to balance the total inspection distance and the distance standard deviation of UAVs, where the outer optimization employs the NSGA-II to assign task units to each UAV evenly, while the inner optimization deploys an adaptive genetic algorithm with an elite retention strategy to optimize the inspection direction and order in each task domain to obtain a Pareto-optimal solution set under constraints. …”
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Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios by Wacław Kuś, Waldemar Mucha, Iyasu Tafese Jiregna

“…This paper focuses on optimizing sensor placement in structures for load monitoring applications. …”
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Research on Mobile Robot Path Planning Based on MSIAR-GWO Algorithm by Danfeng Chen, Junlang Liu, Tengyun Li, Jun He, Yong Chen, Wenbo Zhu

“…Finally, the elimination and relocation strategy based on stochastic center-of-gravity dynamic reverse learning is introduced for the inferior individuals in the population, which effectively maintains the diversity of the population and improves the convergence speed of the algorithm while avoiding falling into the local optimal solution effectively. …”
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