Solving Integer Ambiguity Based on an Improved Ant Lion Algorithm by Wuzheng Guo, Yuanfa Ji, Xiyan Sun, Xizi Jia

“…First, the performance of the SAALO algorithm was evaluated by comparing its solving speed and success rate with those of the Ant Lion Optimization Algorithm (ALO), the LAMBDA algorithm and the MLAMBDA algorithm. …”
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A Golden Jackal based optimal operation and optimization of a virtual power plant involving renewable resources by Anubhav Kumar Pandey, Chaima Mansour, Nandini K. K., Vinay Kumar Jadoun, Jayalakshmi N. S.

“…In particular, there is a reduction in cost by 2.1% and 0.62% when compared with the salp swarm algorithm and beluga whale algorithm followed by 2.12% and 1.18% in emission. …”
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Comprehensive Research on Energy Saving Synergy Optimization and Optimal Real-time Control of Hybrid Electric Mining Trucks by Li Hongliang, Zhang Guojing, Fan Ping

“…This study takes the hybrid electric mining truck as a research object, establishes the mathematical model of the whole vehicle, takes reducing fuel consumption as the goal, establishes a collaborative optimization model that comprehensively considers the interaction between transmission system parameters and energy management strategies, and combines particle swarm optimization (PSO) algorithm and dynamic programming (DP) algorithm to build a two-layer interactive optimization algorithm to eliminate the interaction between transmission system parameters and energy management strategies. …”
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Advancing Pile-Bearing Capacity Prediction with Meta-Heuristic Enhanced Specific ANFIS strategys by Zhongmiao Dang

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Research on Fire Detection of Cotton Picker Based on Improved Algorithm by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song

“…The sensor measurements were compared with the actual values to verify the effectiveness of the GWO-PSO-optimized BP neural network model. Once experimentally verified, the improved GWO-PSO algorithm achieves a correlation coefficient R of 0.96929, a prediction accuracy rate of 96.10%, and a prediction error rate of only 3.9%, while the system monitors an accurate early warning rate of 96.07%, and the false alarm and omission rates are both less than 5%. …”
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Application of Artificial Intelligence Computer Intelligent Heuristic Search Algorithm by Fanghai Gong

“…After 114 iterations of the heuristic search algorithm, the optimal value is 6.83353635e-001, while the average optimal value is 6.83551939e-001. …”
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AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection by Fujiang Yuan, Zihao Zuo, Yang Jiang, Wenzhou Shu, Zhen Tian, Chenxi Ye, Junye Yang, Zebing Mao, Xia Huang, Shaojie Gu, Yanhong Peng

“…Artificial intelligence, with its powerful data processing capability, pattern recognition ability, and adaptive optimization algorithms, can improve the transaction processing efficiency of blockchain, enhance the security mechanism, and optimize the privacy protection strategy, thus effectively alleviating the limitations of blockchain in terms of scalability and security. …”
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Bio-Inspired Metaheuristics in Deep Learning for Brain Tumor Segmentation: A Decade of Advances and Future Directions by Shoffan Saifullah, Rafał Dreżewski, Anton Yudhana, Wahyu Caesarendra, Nurul Huda

“…This review systematically examines developments from 2015 to 2025, focusing on the integration of nature-inspired optimization methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and novel hybrids including CJHBA and BioSwarmNet into deep learning-based brain tumor segmentation frameworks. …”
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Honeyword Generation Using a Proposed Discrete Salp Swarm Algorithm by Yasser A. Yasser, Ahmed T. Sadiq, Wasim AlHamdani

“…This work will present a novel honeyword generation method that uses a proposed discrete salp swarm algorithm. The salp swarm algorithm (SSA) is a bio-inspired metaheuristic optimization algorithm that imitates the swarming behavior of salps in their natural environment. …”
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Optimizing Investment Portfolios with Bacterial Foraging and Robust Risk Management by Hubert Zarzycki

“…BFO, a nature-inspired algorithm, is employed to construct diversified portfolios, while risk management strategies, including stop-loss limits and transaction cost considerations, enhance risk control. …”
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Dynamically optimal models of atmospheric motion by A. G. Voronovich

“…</p></li><li> <p id="d2e91">The spatial resolution can be adjusted locally while executing calculations.</p></li><li> <p id="d2e95">By using a set of tetrahedra as finite elements the algorithm ensures a better representation of the topography (piecewise linear rather than staircase).…”
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MODELING AND OPTIMIZATION OF VEHICLE SUSPENSION OSCILLATIONS by N. N. Hurski, A. Kader Karami

“…Results on optimization of suspension oscillations by minimum vibro-acceleration  of cushioned mass while a vehicle is moving within the preset speed interval are presented in the paper.…”
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Optimal policy for composite sensing with crowdsourcing by Bei Zhao, Siwen Zheng, Jianhui Zhang

“…The solution includes optimal and myopic policies. Besides, we provide necessary theoretical analysis, which ensures the optimality of the optimal algorithm. …”
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Hardware reconfigurable coding and evolution algorithm based on evolvable hardware by Ting WANG, Ju-long LAN, Jun-ting WU

“…A planar mapping function increments chromosome coding method based on FPGA platform with SRAM-architecture was proposed.The method could improve hardware reconfiguration efficiency by coding mapping realized by double platform mapping of binary configurable file string.Meanwhile,a betterment difference evolution algorithm was proposed based on local optimal mechanism introduced.The algorithm could promote convergence rate and whole efficiency.Finally,the result of the algorithm emulation shows that:MDE improves disadvantage of difference evolution algorithm with local optimal and approaches actual optimization outcome.…”
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Analysis of optimal configuration of energy storage in wind-solar micro-grid based on improved gray wolf optimization by Huang Qian, Huang Dengke, Cai Li, Xu Qingshan

“…Comparing the difference between energy storage without an installation and energy storage with improved algorithm, it is shown that the energy storage configuration of the improved gray wolf optimization improves the economy, efficient energy use, and revenue of the whole system.…”
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Hybrid Adaptive Sheep Flock Optimization and Gradient Descent Optimization for Energy Management in a Grid-Connected Microgrid by Sri Harish Nandigam, Krishna Mohan Reddy Pothireddy, K. Nageswara Rao, Surender Reddy Salkuti

“…The ASFO algorithm offers robust global search capabilities to explore complex search spaces, while GDO safeguards precise local convergence to optimize the dispatch schedule and energy cost and maximize renewable energy utilization. …”
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Stochastic Block-Coordinate Gradient Projection Algorithms for Submodular Maximization by Zhigang Li, Mingchuan Zhang, Junlong Zhu, Ruijuan Zheng, Qikun Zhang, Qingtao Wu

“…Due to high-dimensional data, the computation of the whole gradient vector can become prohibitively expensive. …”
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Specifics of the Algorithmic Prescriptions in the Learning Process of the Gymnastics Exercises’ Techniques by Victor BUFTEA

“… The technology of training gymnastic exercises demonstrates a series of groupings of the most rational and optimal methods; however, the algorithmic type prescriptions are considered the closest to the effective achievement of the theoretical-methodological and practical objectives (in order to acquire the technique of gymnastic exercises with an increased degree of difficulty). …”
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Multi-Feature Facial Complexion Classification Algorithms Based on CNN by Xiyuan Cao, Delong Zhang, Chunyang Jin, Zhidong Zhang, Chenyang Xue

“…The optimal approach combining multi-feature CNN with machine learning algorithms attains a remarkable accuracy of 97.78%. …”
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Distributed Constraint Optimization with Flocking Behavior by Zhengquan Yang, Qing Zhang, Zengqiang Chen

“…Then, it is proved that all agents track the optimal velocity while avoiding collisions. The proof of the main result is divided into three steps: global set convergence, consensus analysis, and optimal set convergence. …”
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