A Chemistry-Based Optimization Algorithm for Quality of Service-Aware Multi-Cloud Service Compositions by Mona Aldakheel, Heba Kurdi

“…These results validate the proposed approach’s effectiveness in optimizing service composition while minimizing computational overhead in multi-cloud environments.…”
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Real-Time Drilling Performance Optimization Using Automated Penetration Rate Algorithms with Vibration Control by Dan Sui

“…Automation has transformed process optimization across industries by enhancing efficiency, safety, and reliability while minimizing human intervention. …”
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DGA-ACO: Enhanced Dynamic Genetic Algorithm—Ant Colony Optimization Path Planning for Agribots by Zhenpeng Zhang, Pengyu Li, Shanglei Chai, Yukang Cui, Yibin Tian

“…A multi-objective fitness function simultaneously optimizes path length, energy efficiency, and safety metrics, while genetic operators prevent algorithmic stagnation. …”
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An Information-Extreme Algorithm for Universal Nuclear Feature-Driven Automated Classification of Breast Cancer Cells by Taras Savchenko, Ruslana Lakhtaryna, Anastasiia Denysenko, Anatoliy Dovbysh, Sarah E. Coupland, Roman Moskalenko

“…These features were then used to classify cells as normal or malignant using an information-extreme algorithm. This algorithm optimizes an information criterion within a binary Hamming space to achieve robust recognition with minimal input features. …”
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Optimizing stochastic multi-project scheduling with a simulation integrated multi-objective genetic algorithm by Mudassar Rauf, Muhammad Imran, Jabir Mumtaz, Saima Javed

“…The effectiveness of the proposed SIHMO-GA is evaluated through a real-world case study by comparing it with simulation-optimization approaches, namely the multi-objective genetic algorithm (MOGA) and goal attainment programming. …”
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Spatiotemporally Combined Dimensionality Reduction Algorithm for Optimizing Long-term Operation of Multi-reservoir Systems by CHEN Jia, ZHANG Hanjun, XU Nan

“…To alleviate the “curse of dimensionality” and improve the solution efficiency while ensuring the quality of solutions in optimizing the operation of multi-reservoir systems,this paper proposes a spatiotemporally combined dimensionality reduction algorithm which integrates and improves the dynamic programming with successive approximation (DPSA) and the progressive optimality algorithm (POA).First,a chain-based successive approximation strategy is proposed to expand the DPSA's optimization mode from “single reservoir alternation” to “cascade reservoir chain alternation”,which makes up for the DPSA's shortcomings in dealing with the hydraulic coupling relationships among cascade reservoirs.Then,a dynamic variable decoupling strategy and perturbation mechanism are proposed to deal with the POA's blind search problem and dimensionality problem.Finally,the two improved algorithms are combined,in which the improved POA is applied to solving the optimization problems of cascade reservoir chains under the framework of the improved DPSA.The power generation operation problem of the cascade reservoirs in the Yuan River Basin of Hunan Province and the classical ten-reservoir problem are utilized to test the performance of the proposed algorithm.The proposed algorithm outperforms seven existing alternatives in terms of solution quality and efficiency.The results indicate that the proposed algorithm can effectively alleviate the “curse of dimensionality” in optimizing the operation of multi-reservoir systems,improve the efficiency while ensuring the quality of solutions and has potential to be applied to optimizing the operation of complex large-scale multi-reservoir systems.…”
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Optimizing protein-ligand docking through machine learning: algorithm selection with AutoDock Vina by Ala’ Omar Hasan Zayed

“…While numerous algorithms exist for protein-ligand docking, achieving an optimal balance between accuracy and computational speed remains challenging. …”
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QELPS Algorithm: A Novel Dynamic Optimization Technology for Quantum Circuits Scheduling Engineering Problems by Zuoqiang Du, Xingjie Li, Hui Li

“…We propose a collaborative optimization framework that integrates the Quantum Exchange Lock Parallel Scheduler (QELPS) with the Full-level Joint Optimization SWAP Algorithm (FJOSA). …”
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Learning-Driven Algorithm With Dual Evolution Patterns for Solving Large-Scale Multiobjective Optimization Problems by Mingshuo Song, Wei Song, Khin Wee Lai

“…Recently, a series of evolutionary algorithms have been proposed to enhance the search efficiency when handling large-scale multiobjective optimization problems (LSMOPs). …”
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Overlapping community-based fair influence maximization under a multi-transformation optimization algorithm by Chunfeng Jiang, Zegang Niu, Jingru Qu, Yulan Zhao, Amin Rezaeipanah

“…Specifically, the proposed algorithm demonstrates a 9.2% improvement in average influence spread over the best existing method, while effectively addressing the trade-offs between influence, fairness, and complexity.…”
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Optimizing Feature Selection and Machine Learning Algorithms for Early Detection of Prediabetes Risk: Comparative Study by Mahmoud B Almadhoun, MA Burhanuddin

“…PCA kept 12 components while maintaining 95% of the variance in the dataset. …”
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AI-driven genetic algorithm-optimized lung segmentation for precision in early lung cancer diagnosis by Yahia Said, Riadh Ayachi, Mouna Afif, Taoufik Saidani, Saleh T. Alanezi, Oumaima Saidani, Ali Delham Algarni

“…This study presents an advanced AI-driven framework, optimized through genetic algorithms, for precise lung segmentation in early cancer diagnosis. …”
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Multiphase Transport Network Optimization: Mathematical Framework Integrating Resilience Quantification and Dynamic Algorithm Coupling by Linghao Ren, Xinyue Li, Renjie Song, Yuning Wang, Meiyun Gui, Bo Tang

“…Next, we create a dynamic adaptive public transit optimization model using an entropy weight-TOPSIS decision framework coupled with an improved simulated annealing algorithm (ISA-TS), achieving coordinated suburban–urban network optimization while maintaining 92.3% solution stability under simulated node failure conditions. …”
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Application of bioinspired global optimization algorithms to the improvement of the prediction accuracy of compact extreme learning machines by L. A. Demidova, A. V. Gorchakov

“…The replacement of the iterative learning process currently used in many neural network architectures with the random initialization of input weights and the explicit computation of output weights significantly increases the performance of this novel machine learning algorithm while preserving good generalization performance. …”
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Multi-objective optimization analysis of construction management site layout based on improved genetic algorithm by Hui Yin

“…In order to improve the rationality of on-site layout, a multi-objective optimization model combining ant colony algorithm and Pareto optimal solution was constructed based on genetic algorithm, and this model was applied to practical engineering cases. …”
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Optimal Charging Algorithm Based on Receding Horizon Framework for Electric Two-Wheeler Charging Stations by Van Nguyen Ngoc, Duc Nguyen Huu

“…By leveraging receding horizon framework combined with grouping approach, the proposed algorithm can effectively perform optimal scheduling while covering the uncertainties of arrival/departure and energy level variation of vehicles. …”
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Optimization of lubricating oil change intervals for marine internal combustion engines: algorithm research and design by Justas Žaglinskis, Laurynas Jagėla

“…This study investigates an algorithm for optimal oil change intervals in the marine engine fleet, which reduces the costs due to the recommendations of ship engine manufacturers, which may have a large margin of error. …”
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A Prescribed-Time Consensus Algorithm for Distributed Time-Varying Optimization Based on Multiagent Systems by Yanling Zheng, Siyu Liu, Jie Zhong

“…A novel continuous-time distributed optimization algorithm is developed based on prescribed-time consensus, guaranteeing the consensus attainment among agents within a user-defined timeframe while asymptotically converging to the time-dependent optimal solution. …”
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End-to-end protocol for high-quality quantum approximate optimization algorithm parameters with few shots by Tianyi Hao, Zichang He, Ruslan Shaydulin, Jeffrey Larson, Marco Pistoia

“…The quantum approximate optimization algorithm (QAOA) is a quantum heuristic for combinatorial optimization that has been demonstrated to scale better than state-of-the-art classical solvers for some problems. …”
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Using PSO and Genetic Algorithms to Optimize ANFIS Model for Forecasting Uganda’s Net Electricity Consumption by Kürşat Ayan, Abdal Kasule

“…We use particle swarm optimization (PSO) algorithm and genetic algorithm (GA) to optimize the parameters of the model. …”
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