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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Application of an improved ant colony algorithm based on unevenly distributed pheromone and multi-objective optimization in path planning for unmanned surface vehicles by Guobing XIE, Wei HE, Wangwen HU, Yixin Su, Binghua SHI

“…ObjectiveTo address the challenges of path planning for unmanned surface vehicles in complex waters, this paper proposes an improved ant colony optimization（ACO）algorithm based on uneven distributed pheromone and multi-objective optimization.MethodsFirst, a probabilistic roadmap method (PRM) is used to generate an initial path. …”
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Integrating Bioengineering and Machine Learning: A Multi-Algorithm Approach to Enhance Agricultural Sustainability and Resource Efficiency by Senthil G.A., Prabha R., Asha R.M., Suganthi S.U., Sridevi S.

“…The novel research incorporates high-level machine learning algorithms for optimizing agricultural performance regarding sustainability and resource efficiencies. …”
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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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An improved multiobjective evolutionary algorithm for time-dependent vehicle routing problem with time windows by Jia-ke Li, Jun-qing Li, Ying Xu

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Efficiently Automating Covering Array Generation: A Model Checking and Metaheuristic Algorithm Approach by Sajad Esfandyari, Davar Giveki, Amir Rastegarnia, Ali Farzamnia, Hua Zheng

“…The GROOVE model checker extracts parameters and their values, which are then optimized by the BBO and GA algorithms to generate a minimal and effective test suite. …”
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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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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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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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A new smart charging electric vehicle and optimal DG placement in active distribution networks with optimal operation of batteries by Bilal Naji Alhasnawi, Marek Zanker, Vladimír Bureš

“…Thirdly, this paper proposes an optimization technique based on the Mountain Gazelle Optimizer (MGO), Improved Beluga Whale Optimization (IBWO), and Arithmetic Optimization Algorithm (AOA) for determining the optimal DGs in radial distribution systems. …”
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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 and Dynamic Adjustment of Tandem Columns for Separating an Ethylbenzene–Styrene Mixture Using a Multi-Objective Particle Swarm Algorithm by Guangsheng Jiang, Yibo She, Zhongwen Song, Liwen Zhao, Guilian Liu

“…A steady-state model is developed to minimize total energy consumption (TEC) and total annualized cost (TAC) by optimizing the reflux flow rates. An integrated dynamic model is created using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. …”
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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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MaOSSA: A new high-efficiency many-objective salp swarm algorithm with information feedback mechanism for industrial engineering problems by Mohammad Aljaidi, Janjhyam Venkata Naga Ramesh, Ajmeera Kiran, Pradeep Jangir, Arpita, Sundaram B. Pandya, Wulfran Fendzi Mbasso, Laith Abualigah, Ali Fayez Alkoradees, Mohammad Khishe

“…The strategy enables control of convergence and diversity while simultaneously adapting to alterations in the Pareto front. …”
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Enhanced multi-objective Evolutionary Mating Algorithm with improved crowding distance and Levy flight for optimizing comfort index and energy consumption in smart buildings by Muhammad Naim Bin Nordin, Mohd Herwan Sulaiman, Nor Farizan Zakaria, Zuriani Mustaffa

“…While current Evolutionary Mating Algorithms (EMA) primarily focus on single-objective optimization and rely on weighted functions for handling multiple objectives, such approaches prove impractical for the complex trade-offs between comfort index and energy efficiency. …”
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EEG channels selection for stroke patients rehabilitation using equilibrium optimizer by Al-Betar Mohammed Azmi, Alyasseri Zaid Abdi Alkareem, Makhadmeh Sharif Naser

“…This article addresses this problem by presenting an innovative optimization-based approach to the channel selection problem, employing a novel binary equilibrium optimizer (EO) as an optimization technique to identify the most relevant EEG channels. …”
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