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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Usage of the optimal fuzzy system models on chloride diffusion of concrete by Shuai Sun

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Optimized integration of photovoltaic systems and distribution static compensators in distribution networks using a novel discrete-continuous version of the adaptive JAYA algorithm... by Paolo Iván Cubillo-Leyton, Oscar Danilo Montoya, Luis Fernando Grisales-Noreña

“…For comparison, the discrete-continuous versions of the vortex search algorithm (VSA), the sine-cosine algorithm (SCA), Phasor Particle Swarm Optimization (PPSO), New Self-Organising Hierarchical PSO (NHPSO-JTVAC), Differential Evolution (DE), Gold Search Optimization (GSO) and the classical version of JAYA are used, validating the effectiveness of our proposal on the 33- and 69-bus test systems while considering variable generation and demand. …”
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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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Research on Early Diagnosis Methods for Broiler Chicken Diseases Based on Swarm Intelligence Optimization Algorithms and Random Forest by X Peng, C Chen, L Yu, X Kong, B Sun

“…A baseline Random Forest (RF) model achieved 94.01% diagnostic accuracy for broiler diseases. To optimize performance, we developed RF_WOA_DBO-an integrated algorithm combining RF with enhanced Whale Optimization Algorithm (WOA) for global feature selection and modified Dung Beetle Optimizer (DBO) for local parameter tuning. …”
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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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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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Intelligent Modeling and Experimental Investigation of the Collar's Impact on Reducing Scour around the Spur Dike by Hojat Karami, Alireza Rezaei, Amin Atarodi

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The Bregman Modified Second APG Method for DC Optimization Problems by Lumiao Wang, Ziye Liu, Chunguang Liu

“…To address this limitation, researchers often employ Bregman distance as an alternative to Euclidean distance in existing DC algorithms. While this substitution relaxes the requirements on DC functions, it simultaneously introduces greater complexity in theoretical analysis. …”
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Optimized and Routed Wiring Harness Based on Zonal Clustering Concept Using AI in the Automotive Industry by Md Sanowar Hossain, Hafiz Abdul Quddus, Ziya Cevahir, Alexander Jesser

“…A methodology integrating K-means clustering with dynamic grid-based routing algorithms (A* and Bresenham&#x2019;s line algorithm) is applied to optimize the wiring harness layout across the vehicle&#x2019;s zones, covering six key domains (comfort, chassis, drive assist, infotainment, system, and powertrain). …”
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Smart Energy Strategy for AC Microgrids to Enhance Economic Performance in Grid-Connected and Standalone Operations: A Gray Wolf Optimizer Approach by Sebastian Lobos-Cornejo, Luis Fernando Grisales-Noreña, Fabio Andrade, Oscar Danilo Montoya, Daniel Sanin-Villa

“…To assess performance, 100 independent runs per method were conducted, comparing GWO against particle swarm optimization (PSO) and genetic algorithms (GAs). Statistical analysis confirmed that GWO achieved the lowest operational costs (USD 3299.39 in grid-connected mode and USD 11,367.76 in islanded mode), the highest solution stability (0.19% standard deviation), and superior voltage regulation. …”
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A Bi-Level Capacity Optimization Method for Hybrid Energy Storage Systems Combining the IBWO and MVMD Algorithms by Qiaoqiao Xing, Shidong Li, Da Qiu, Yang Long, Qinyi Liao, Xiangjin Yin, Yunxiang Li, Kai Qian

“…The inner layer builds on the optimized results from the outer layer, employing a Multivariable Variational Mode Decomposition (MVMD) algorithm to regulate the power output of the energy storage system. …”
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Investigation and optimal design of band gap tunability in fractal phononic crystals by Yang Shuai, Yin Jia-Hao, Zhu Xiao-Jing, Wang Kai, Zhang Shi-ke, Cao Lu, Guo Peng-Yu, Liu Yong

“…Furthermore, this study employs genetic algorithm optimization to achieve the maximum band gap width at different frequencies, enhancing the practical value of fractal phononic crystals in engineering applications. …”
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Application of Artificial Intelligence with Ant Colony Algorithm in construction projects schedule by Kiana Ahghari

“…So useMethodology of Scheduling of Projects with Artificial Intelligence and with the Approach of Ant Colony Algorithm for Organizations MethodOptimal and practical among other methods. …”
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Optimizing the Design of TES Tanks for Thermal Energy Storage Applications Through an Integrated Biomimetic-Genetic Algorithm Approach by Nadiya Mehraj, Carles Mateu, Gabriel Zsembinszki, Luisa F. Cabeza

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GA-VAE: Enhancing Local Feature Representation in VQ-VAE Through Genetic Algorithm-Based Token Optimization by Jinghui Jiang, Dongjoon Kim, Bohyoung Kim, Yeong-Gil Shin

“…Our proposed Codebook Genetic Algorithm (CGA) selectively optimizes these underutilized tokens by applying genetic operations to enhance their representation capability while maintaining the well-learned features of frequently used tokens. …”
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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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Reliability Analysis of Three-Dimensional Slopes Considering the Soil Spatial Variability Based on Particle Swarm Optimization Algorithm by Xin Chen, Jiale Xu, Yukuai Wan, Rong Yang, Jiewen Sun, Di Wu

“…This paper presents a new algorithm for assessing the reliability of three-dimensional (3D) slope stability considering the spatial variability of soil based on the Particle Swarm Optimization (PSO) algorithm. …”
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Daily Runoff Prediction Model Based on Multivariate Variational Mode Decomposition and Correlation Reconstruction by DING Jie, TU Peng-fei, FENG Yu, ZENG Huai-en

“…[Conclusions](1) The MVMD decomposition method can control the number of decomposition layers, ensuring complete signal feature extraction without overfitting while improving processing speed.(2) Pearson correlation coefficient method enhances prediction accuracy through decomposed data classification.(3) The MEA-BP can improve signal-to-noise ratio, adapt to complex environments, enhance learning efficiency and generalization ability, and reduce computational complexity.(4) The GWO-ELM algorithm integrates grey wolf optimizer with extreme learning machine, providing a fast and adaptive solution for time-series prediction with reduced overfitting and improved efficiency.(5) The overall combined model can efficiently and stably process large amount of data while ensuring high accuracy.…”
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