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International Symposium on Logistics and Industrial Informatics

作者： Csiszar, Sandor Budapest Tech Kalman Kando Fac Elect Engn Inst Microelect & Technol H-1084 Budapest Hungary

ISBN: (纸本)9781424414406

Nowadays an increasing number of papers appears in the subject of combinatorial optimization proposing a great variety of heuristics and metaheuristics, most of them apply special solution to fit the particular problem type. The analysis points out the importance of the generalization but the special intelligence in the algorithm design is still very important. Although the navigation in the solution space can be realized implicitly it has a decisive role in the performance. It is important to note that the success of sophisticated methods is often reduced by their relatively bad explorative capability. The well known algorithms are compared and also the latest most successful methods based on fast and simple heuristics- are discussed.

关键词： Algorithm design Solution space optimization algorithms Heuristics navigation role performance Generalization Fasting Intelligence

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A Probabilistic Approach to Load Balancing in Multi-Cloud Environments via Machine Learning and optimization algorithms

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JOURNAL OF GRID COMPUTING 2025年第2期23卷 1-36页

作者： Sefati, Seyed Salar Nor, Ahmed M. Arasteh, Bahman Craciunescu, Razvan Comsa, Ciprian-Romeo Natl Univ Sci & Technol POLITEHNICA Bucharest Fac Elect Telecommun & Informat Technol Telecommun Dept Bucharest 060042 Romania Istinye Univ Fac Engn & Nat Sci Dept Software Engn TR-34460 Istanbul Turkiye Khazar Univ Dept Comp Engn AZ-1096 Baku Azerbaijan Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan Gheorghe Asachi Tech Univ Iasi Dept Telecommun & Informat Technol Iasi 700506 Romania

Efficient load balancing stands out as a crucial challenge in multi-cloud environments, particularly for applications that demand ultra-reliable, low-latency communications (URLLC). This paper proposes a novel approach integrating Decision Functions with Normal Distributions (DFND) for precise probabilistic modeling of task-to-cloud compatibility. Multivariate normal distributions capture interdependencies between resource features such as CPU, memory, bandwidth, and latency, ensuring accurate resource compatibility evaluation. Additionally, the Tasmanian Devil optimization (TDO) algorithm employs dynamic exploration and exploitation strategies inspired by natural behaviors, providing rigorous optimization to improve task assignment in dynamic, multi-cloud environments. It uses flexible methods to ensure the optimization process is both efficient and scalable. Simulation results using CloudSim demonstrate significant improvements over state-of-the-art methods in terms of makespan reduction, response time minimization, resource utilization, and cost efficiency. The proposed framework effectively supports latency-sensitive, large-scale applications in dynamic, heterogeneous multi-cloud environments.

关键词： Multi-cloud environments Load balancing Ultra-reliable low-latency communications (URLLC) Probabilistic modeling optimization algorithms Resource utilization

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Chaos-enhanced white shark optimization algorithms CWSO for global optimization

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ALEXANDRIA ENGINEERING JOURNAL 2025年 122卷 465-483页

作者： El Maloufy, Ahmed Bencherqui, Ahmed Tahiri, Mohamed Amin El Ghouate, Nawal Karmouni, Hicham Sayyouri, Mhamed Askar, S. S. Abouhawwash, Mohamed Sidi Mohamed Ben Abdellah Fez Univ Natl Sch Appl Sci Lab Engn Syst & Applicat Fes Morocco Cadi Ayyad Univ Natl Sch Appl Sci Marrakech Morocco King Saud Univ Coll Sci Dept Stat & Operat Res POB 2455 Riyadh 11451 Saudi Arabia Michigan State Univ Dept Anim Sci E Lansing MI 48824 USA Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt

Metaheuristic optimization algorithms are vital across various domains but often struggle with convergence to local optima, limiting their potential to discover globally optimal solutions. Integrating chaotic maps into the optimization process has proven particularly advantageous, as it broadens search capabilities, accelerates convergence, and reduces the likelihood of getting trapped in local minima. We present an optimized algorithm, the Chaotic White Shark Optimizer (CWSO), which incorporates ten different chaotic maps to replace random sequences in key components of the standard White Shark Optimizer (WSO). This modification aims to effectively balance the exploration and exploitation phases, thereby enhancing the probability of finding globally optimal solutions. The CWSO was evaluated on 23 benchmark functions and applied to engineering problems, demonstrating its robustness and reliability. Furthermore, it was used for reconstructing signals and 2D/3D medical images. Comparative evaluations with six well-known metaheuristic algorithms showed that the CWSO outperformed the original WSO and other existing algorithms, offering superior performance in terms of solution quality, global optimality, and avoiding local minima.

关键词： optimization algorithms Metaheuristics Chaotic maps Signal processing White Shark Optimizer

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A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic optimization algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and Deep Learning Paradigms

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IEEE ACCESS 2025年 13卷 35750-35779页

作者： Mathur, Priya Shaikh, Hammad Sheth, Farhan Kumar, Dheeraj Gupta, Amit Kumar Poornima Inst Engn & Technol Jaipur 302022 Rajasthan India Manipal Univ Jaipur Dept Comp Sci & Engn Jaipur 303007 Rajasthan India

This research presents a robust and comprehensive framework for predicting the density of hybrid nanofluids using state-of-the-art machine learning and deep learning techniques. Addressing the limitations of conventional empirical approaches, the study used a curated dataset of 436 samples from the peer-reviewed literature, which includes nine input parameters such as the nanoparticle, base fluid, temperature (degrees C), volume concentration ( phi ), base fluid density ( rho bf ), density of primary and secondary nanoparticles ( rho(np1) and rho(np2) ), and volume mixture ratios of primary and secondary nanoparticles. Data preprocessing involved outlier removal via the Interquartile Range (IQR) method, followed by augmentation using either autoencoder-based or Gaussian noise injection, which preserved statistical integrity and enhanced dataset diversity. The research analyzed fourteen predictive models, employing advanced hyperparameter optimization methods facilitated by Grey Wolf optimization (GWO) and Particle Swarm optimization (PSO). In particular, autoencoder-based augmentation combined with hyperparameter optimization consistently improved predictive accuracy across all models. For machine learning models, Gradient Boosting achieved the most remarkable performance, with R-2 scores of 0.99999 and minimal MSE values of 0.00091. Among deep learning models, Recurrent Neural Networks (RNN) stacked with Linear Regression achieved superior performance with an R-2 of 0.9999, MSE of 0.0014, and MAE of 0.012. The findings underscore the synergy of advanced data augmentation, meta-heuristic optimization, and modern predictive algorithms in modelling hybrid nanofluid density with unprecedented precision. This framework offers a scalable and reliable tool for advancing nanofluid-based applications in thermal engineering and related domains.

关键词： Autoencoders Nanoparticles Predictive models Machine learning Fluids Prediction algorithms Gaussian noise Deep learning Data models Data augmentation Density prediction hybrid nanofluids machine learning deep learning data augmentation meta-heuristic optimization optimization algorithms thermal engineering

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Enhanced Multi-objective Artificial Physics optimization algorithms for Solution Set Distributions

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第6期49卷 175-190页

作者： Zhang, Yunpeng Innovation and Entrepreneurship Center North China Institute of Aerospace Engineering Langfang065000 China

To optimize the distribution of solution sets in multi-objective optimization algorithms, this study takes the artificial physics optimization algorithm as an example, and introduces the elite learning, inverse learning, bi-directional speed, and chaotic mutation strategies to guide the evolution and search of individuals. This study guides the algorithm to carry out multi-objective solving from three aspects: external archive set, optimal guided individual selection, and step size optimization. The experimental outcomes denote that the improved artificial physics optimization algorithm designed in this study has the best minimum convergence value, standard deviation, optimization times, and convergence performance on the test function. The population fitness curve performs well. At the same time, the super volume index is 0.958, and the solution running time on different test functions is 6.13s, 7.61s, 8.46s, 9.68s, and 10.77s, respectively, with the smallest value. The improved multi-objective artificial physics optimization algorithm achieves the lowest Generative Distance value of 0.126, the lowest Inverted Generative Distance value of 0.171, the lowest extensiveness evaluation value of 0.210, and the maximum distributiveness evaluation value of 0.989. The actual solution coverage is high. This study expands the solution ideas and methods for multi-objective optimization problems, significantly improving the performance of artificial physics optimization algorithms in solving multi-objective optimization problems. © 2025 Slovene Society Informatika. All rights reserved.

关键词： optimization algorithms

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AI optimization algorithms enhance higher education management and personalized teaching through empirical analysis

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SCIENTIFIC REPORTS 2025年第1期15卷 1-18页

作者： Xu, Xiwen Sungkyunkwan Univ Dept Educ Seoul 03063 South Korea

This research investigates the application of artificial intelligence (AI) optimization algorithms in higher education management and personalized teaching. Through a comprehensive literature review, theoretical analysis, and empirical study, the potential, effectiveness, and challenges of integrating AI algorithms into educational processes and systems are explored. The study demonstrates that AI optimization algorithms can effectively solve complex educational management problems and enable personalized learning experiences. An empirical study conducted over one academic semester shows significant improvements in students' learning outcomes, engagement, satisfaction, and efficiency when using AI-driven personalized teaching compared to traditional approaches. The research also identifies challenges and limitations, including data privacy issues, algorithmic bias, and the need for human-AI interaction. Recommendations for future research directions are provided, emphasizing the importance of developing more adaptive algorithms, investigating long-term effects, and establishing ethical frameworks for AI in education.

关键词： Artificial intelligence optimization algorithms Higher education management Personalized teaching Learning analytics Educational data mining Adaptive learning Resource allocation Student engagement Empirical study

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Active polarization controlling in optical fiber links using optimization algorithms

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INDIAN JOURNAL OF PHYSICS 2025年第4期99卷 1471-1478页

作者： Asgari, Hamid Khodabandeh, Majid Hajibaba, Soheil Dadahkhani, Amir Hosein Madani, Seyed Ahmad Iranian Ctr Quantum Technol ICQTs Quantum Commun Grp Tehran Iran

Maintaining the state of polarization of a light beam in optical fiber links is a necessity for various applications. It is especially important for optical communication, polarization-based quantum key distribution, etc. There are several ways to compensate for polarization fluctuations, hence lowering the polarization drift error. In this paper, we have experimentally investigated the performance of two optimization algorithms for polarization compensation. We employed these algorithms to control an electrical polarization controller. Our results show that the particle swarm optimization and simulated annealing algorithms can efficiently compensate for polarization fluctuations and drifts. Thus, it is suitable for optical communication and quantum key distribution experiments.

关键词： Polarization controlling Polarization compensation Optical fiber links optimization algorithms

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The Application of Bio-Inspired optimization algorithms in Structural optimization Mathematical Models

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 130-142页

作者： Liu, Ji Wang, Wen-Tao Liu, Yang Department of Basic Courses Hebei Vocational University of Industry and Technology Shijiazhuang050091 China Academic Affairs Office Hebei Vocational University of Industry and Technology Shijiazhuang050091 China College of Management Science University of Cyberjaya Cyberjaya63000 Malaysia

With the increasing prevalence of bio-inspired optimization algorithms and advancements in computing power, researchers are increasingly adopting these algorithms to tackle structural optimization problems. The core concept involves transforming structural optimization problems into mathematical expressions and utilizing bio-inspired optimization algorithms to efficiently search the solution space, thereby enhancing the overall effectiveness and quality of optimization results. Structural optimization is a pivotal technology aimed at adjusting the design parameters of a structure to maximize its performance within specific constraints. Nevertheless, traditional optimization algorithms encounter limitations when dealing with structural optimization, such as susceptibility to local optima and high computational complexity. Consequently, this paper explores a novel approach by leveraging bio-inspired optimization algorithms to overcome these limitations and improve the effectiveness of solving structural optimization problems. The research presented herein primarily focuses on establishing a mathematical model that captures the essence of the structural optimization problem and employs bio-inspired optimization algorithms to search for optimal solutions within the solution space. These algorithms simulate biological behaviors and evolutionary processes, endowing them with global search capabilities and robustness. To validate the applicability of bio-inspired optimization algorithms in structural optimization, a series of numerical experiments and comparative analyses are performed using real-world structural problems as benchmarks. By comparing bio-inspired optimization algorithms with traditional approaches like gradient-based methods and constraint optimization methods, this paper demonstrates the advantages and effectiveness of bio-inspired optimization algorithms in solving structural optimization problems. The experimental results underscore the ability of bio-in

关键词： optimization algorithms

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Electromagnetically Consistent optimization algorithms for the Global Design of RIS

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IEEE Wireless Communications Letters 2025年第5期14卷 1286-1290页

作者： Shabir, M.W. Di Renzo, M. Zappone, A. Debbah, M. Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes Gif-sur-Yvette91192 France Centre for Telecom. Research Dept. of Engineering King's College London LondonWC2R 2LS United Kingdom Dept. of Electrical and Information Engineering Univ. of Cassino and Southern Lazio Cassino03043 Italy Khalifa University Computer and Information Engineering Abu Dhabi127788 United Arab Emirates

We model a reconfigurable intelligent surface as an inhomogeneous boundary of surface impedance, and consider various optimization problems that offer different tradeoffs in terms of performance and implementation complexity. The considered non-convex optimization problems are reformulated as a sequence of approximating linear quadratically constrained or semidefinite programs, which are proved to have a polynomial complexity and to converge monotonically in the objective value. © 2012 IEEE.

关键词： optimization algorithms

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optimization algorithms based on double-integral revolutionary neurodynamics in deep learning

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Su, Dan Han, Jie Yang, Chunhua Gui, Weihua School of Automation Central South University Changsha410083 China

Deep neural networks are increasingly exposed to attack threats, and at the same time, the need for privacy protection is growing. As a result, the challenge of developing neural networks that are both robust and capable of strong generalization while maintaining privacy becomes pressing. Training neural networks under privacy constraints is one way to minimize privacy leakage, and one way to do this is to add noise to the data or model. However, noise may cause gradient directions to deviate from the optimal trajectory during training, leading to unstable parameter updates, slow convergence, and reduced model generalization capability. To overcome these challenges, we propose an optimization algorithm based on double-integral coevolutionary neurodynamics (DICND), designed to accelerate convergence and improve generalization in noisy conditions. Theoretical analysis proves the global convergence of the DICND algorithm and demonstrates its ability to converge to near-global minima efficiently under noisy conditions. Numerical simulations and image classification experiments further confirm the DICND algorithm's significant advantages in enhancing generalization performance. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： optimization algorithms

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