Quantum-Enhanced Generalized Pattern Search Optimization by Colton Mikes, David Huckleberry Gutman, Victoria E. Howle

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ACOFuzz: An ant colony algorithm-based fuzzer for smart contracts by Peixuan Feng, Wenrui Cao, Siqi Lu, Yongjuan Wang, Haoyuan Xue, Runnan Yang

“…ACOFuzz employs the ant colony optimization (ACO) algorithm to traverse the control flow graph (CFG) of smart contracts, systematically exploring execution paths and generating test cases. …”
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COMQ: A Backpropagation-Free Algorithm for Post-Training Quantization by Aozhong Zhang, Zi Yang, Naigang Wang, Yingyong Qi, Jack Xin, Xin Li, Penghang Yin

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Mathematical Formalization and Algorithmization of the Main Modules of Organizational and Technical Systems by A. A. Solodov

“…This mathematical interpretation of the organizational and technical systems structure allowed us to formalize the main problems solved by typical organizational and technical systems and consider optimal algorithms for solving such problems. The assumption when considering the problems of synthesis of optimal algorithms is to optimize the status assessment module of organizational and technical systems and the control module separately, while the main attention is paid to the consideration of optimal estimation algorithms. …”
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Boosting feature selection efficiency with IMVO: Integrating MVO and mutation-based local search algorithms by Maryam Askari, Farid Khoshalhan, Hodjat Hamidi

“…In this research, we introduce the Improved Multi-Verse Optimizer (IMVO) algorithm, a novel feature selection method that integrates the Multi-Verse Optimizer (MVO) with local search algorithms (LSAs). …”
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The Optimal Cost Design of Reinforced Concrete Beams Using an Artificial Neural Network—The Effectiveness of Cost-Optimized Training Data by Jaemin So, Seungjae Lee, Jonghyeok Seong, Donwoo Lee

“…This study presents a method for the automated design of reinforced concrete (RC) beam cross-sections using an artificial neural network (ANN) trained with cost-optimized data generated by the crow search algorithm (CSA). …”
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Differential evolution-optimized gold nanorods for enhanced photothermal conversion by Aimad Koulali, Piotr Radomski, Paweł Ziółkowski, Francesca Petronella, Luciano De Sio, Dariusz Mikielewicz

“…Utilizing the Differential Evolution (DE) algorithm, a robust metaheuristic optimization approach, we calculated the optimal size and shape of AuNRs for selected wavelengths. …”
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Multicluster Distributed Optimization Strategy for Turbine Wake Environment by Zhenping Yu, Xinmeng Zhou, Yedong Huang, Kunyu Zhou, Guangming Cui, Juntian Qu

“…The experimental results demonstrate that the proposed algorithm can effectively improve the computational efficiency of wake optimization, while ensuring the effect of the wake optimization algorithm in actual wind farms as much as possible, which is more in line with the wake optimization needs of actual wind farms. …”
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Enhancing Injector Performance Through CFD Optimization: Focus on Cavitation Reduction by Jose Villagomez-Moreno, Aurelio Dominguez-Gonzalez, Carlos Gustavo Manriquez-Padilla, Juan Jose Saucedo-Dorantes, Angel Perez-Cruz

“…The integration of optimization algorithms further enhances these processes by facilitating studies on mechanical behavior and accelerating iterative operations. …”
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Solving MRTA problem based on multi-strategy genetic algorithm by CHEN Haiyang, LIU Yan, DOU Wei, HUANG Qi

“…Secondly, the population was divided into three parts to enhance the quality of chromosomes while maintaining randomness. Then, heuristic crossover operators were used to expand the search range of the solution and increase the algorithm′s ability to jump out of local optima. …”
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A computation offloading scheme for energy consumption optimization in Internet of vehicles by Wenxuan GAO, Xinjie YANG

“…In Internet of vehicles (IoV), vehicle-oriented applications are generally computation-intensive and latency-sensitive.Introducing idle computing resources from mobile vehicles as a supplement to network computing power can effectively alleviate the load pressure on edge servers.The problem of task allocation for edge computation offloading in the context of IoV environment were researched.By fully leveraging the combined computing resources of roadside units (RSU), user vehicles, and mobile vehicles within the RSU service range, a computation offloading strategy based on the sparrow search algorithm was proposed and referred to as sparrow search based computation offloading scheme (S²COS), aiming to optimize the overall system energy consumption.In addition, this strategy fully taked into account practical network issues such as service time constraints caused by vehicle mobility and the potential occurrence of computation node failures.The simulation results demonstrate that S²COS can meet the latency requirements for computation-intensive and latency-sensitive tasks, while significantly reducing system energy consumption.…”
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Solving Spatial Optimization Problems via Lagrangian Relaxation and Automatic Gradient Computation by Zhen Lei, Ting L. Lei

“…By automating the (sub)gradient computation, we significantly lower the cost of developing a Lagrangian algorithm for the <i>p</i>-median. And such automation can be applied to numerous other optimization problems.…”
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Optimize multi-objective transformation rules of water-sediment regulation for cascade reservoirs in the Lower Yellow River of China by Kunhui Hong, Aixing Ma, Wei Zhang, Mingxiong Cao

“…An innovative method (ESS-NSGA-II) is proposed, combining Efficient Search Space (ESS) with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize cascade reservoirs operation. …”
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Usage of the optimal fuzzy system models on chloride diffusion of concrete by Shuai Sun

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Optimizing energy efficiency and indoor thermal comfort in rural self-built housing: A comparative study of GA and EA algorithms by Chen Chen, Yuanyuan Wei

“…This study investigates how building design parameters influence electricity consumption, CO2 emissions, and indoor thermal comfort, and compares the performance of Genetic Algorithm (GA) and Evolutionary Algorithm (EA) in optimizing these objectives. …”
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Encryption Algorithm MLOL: Security and Efficiency Enhancement Based on the LOL Framework by Xinyue Zhang, Daoguang Mu, Wenzheng Zhang, Xinfeng Dong

“…In this paper, we present MLOL, a novel AEAD algorithm based on the LOL framework. MLOL combines authenticated encryption with optimizations to the LFSR structure to enhance both security and efficiency. …”
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Meta-transformer: leveraging metaheuristic algorithms for agricultural commodity price forecasting by G. H. Harish Nayak, Md. Wasi Alam, B. Samuel Naik, B. S. Varshini, G. Avinash, Rajeev Ranjan Kumar, Mrinmoy Ray, K. N. Singh

“…To address these challenges, this study proposes a novel framework that combines Transformer models with Metaheuristic Algorithms (MHAs), including the Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO) to enhance agricultural price forecasting accuracy. …”
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A multistrategy improved hunger games search algorithm by Qiu Yihui, Zhang Xinqiang, Li Ruoyu, Li Dongyi, Xia Feihan

“…The proposed MHGS algorithm provides a novel and effective framework for solving complex optimization problems, demonstrating significant theoretical and practical value in the field of computational intelligence.…”
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Optimal D-STATCOM placement and sizing using a novel bird swarm operated exploitation of elephant herd algorithm by V. Tejaswini, D. Susitra

“…In the 33-bus system at hour 14, the BSOEEH model achieved optimal results, surpassing established methods by significant margins: 1.06% superior to Grey Wolf Optimizer (GWO), 1.02% superior to Whale Optimization Algorithm (WOA), 1.02% superior to the GWO-WOA hybrid model, 0.13% superior to Elephant Herding Optimization (EHO), and 0.93% superior to Backtracking Search Algorithm (BSA). …”
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Quantum Selection for Genetic Algorithms Applied to Electromagnetic Design Problems by Gabriel F. Martinez, Alessandro Niccolai, Eleonora L. Zich, Riccardo E. Zich

“…A genetic algorithm with a quantum selection operator that applies high selection pressure while preserving selection diversity is introduced. …”
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