Relevant bibliographies by topics / Intelligent Optimization Algorithms

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Intelligent Optimization Algorithms'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Intelligent Optimization Algorithms"

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Guo, Aipeng, and Chunhui Yuan. "Network Intelligent Control and Traffic Optimization Based on SDN and Artificial Intelligence." Electronics 10, no. 6 (2021): 700. http://dx.doi.org/10.3390/electronics10060700.

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For telecom operators, it is of great significance to employ artificial intelligence (AI) and big data technology in a software-defined network (SDN) in order to achieve intelligent network control, traffic management and optimization. This paper proposes a solution for intelligent work control and traffic optimization. This paper is mainly focused on SDN-based network traffic algorithm optimization and experimental verification. In this paper, we design a network control mechanism for network intelligent control as well as solutions for traffic optimization based on SDN and artificial intelli
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Sobecki, Janusz. "Comparison of Selected Swarm Intelligence Algorithms in Student Courses Recommendation Application." International Journal of Software Engineering and Knowledge Engineering 24, no. 01 (2014): 91–109. http://dx.doi.org/10.1142/s0218194014500041.

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In this paper a comparison of a few swarm intelligence algorithms applied in recommendation of student courses is presented. Swarm intelligence algorithms are nowadays successfully used in many areas, especially in optimization problems. To apply each swarm intelligence algorithm in recommender systems a special representation of the problem space is necessary. Here we present the comparison of efficiency of grade prediction of several evolutionary algorithms, such as: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Intelligent Weed Optimization (IWO), Bee Colony Optimization
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Xu, Minghai, Li Cao, Dongwan Lu, Zhongyi Hu, and Yinggao Yue. "Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization." Biomimetics 8, no. 2 (2023): 235. http://dx.doi.org/10.3390/biomimetics8020235.

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Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biolo
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Peng, Qiang, Renjun Zhan, Husheng Wu, and Meimei Shi. "Comparative Study of Wolf Pack Algorithm and Artificial Bee Colony Algorithm." International Journal of Swarm Intelligence Research 15, no. 1 (2024): 1–24. http://dx.doi.org/10.4018/ijsir.352061.

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Swarm intelligence optimization algorithms have been widely used in the fields of machine learning, process control and engineering prediction, among which common algorithms include ant colony algorithm (ACO), artificial bee colony algorithm (ABC) and particle swarm optimization (PSO). Wolf pack algorithm (WPA) as a newer swarm intelligence optimization algorithm has many similarities with ABC. In this paper, the basic principles, algorithm implementation processes, and related improvement strategies of these two algorithms were described in detail; A comparative analysis of their performance
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Liu, Bingjie, Li Zhu, and Jianlan Ren. "Intelligent optimization algorithm grid computing-based applications." Journal of Intelligent & Fuzzy Systems 39, no. 4 (2020): 5201–11. http://dx.doi.org/10.3233/jifs-189005.

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Optimization algorithms have been rapidly promoted and applied in many engineering fields, such as system control, artificial intelligence, pattern recognition, computer engineering, etc.; achieving optimization in the production process has an important role in improving production efficiency and efficiency and saving resources. At the same time, the theoretical research of optimization methods also plays an important role in improving the performance of the algorithm, widening the application field of the algorithm, and improving the algorithm system. Based on the above background, the purpo
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Bian, Jinhua, and Xiaoxia Zhang. "A Hill-Climbing Differential Evolutionary Algorithm for solving Multiple Travelling Salesman Problem." Frontiers in Computing and Intelligent Systems 9, no. 2 (2024): 4–7. http://dx.doi.org/10.54097/fm6hta13.

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The Multiple Travelling Salesman Problem (MTSP) is a basic deformation of the Travelling Salesman Problem (TSP), which is a typical NP-Hard problem. For combinatorial optimization problems shaped like MTSP, researchers often use intelligent optimization algorithms such as genetic algorithms, differential evolutionary algorithms, simulated annealing algorithms and other intelligent optimization algorithms to approximate the solution. However, typical intelligent optimization algorithms have the disadvantage of being prone to local convergence and failing to produce theoretically optimal solutio
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Kaya, Ebubekir, Ceren Baştemur Kaya, Emre Bendeş, Sema Atasever, Başak Öztürk, and Bilgin Yazlık. "Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking." Biomimetics 8, no. 5 (2023): 402. http://dx.doi.org/10.3390/biomimetics8050402.

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One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in the literature for neural network training. An important group of metaheuristic algorithms is swarm-intelligent-based optimization algorithms. In this study, feed-forward neural network training is carried out for maximum power point tracking by using 13 swarm-intelligent-based optimization al
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Chang, Chang, Tingting Zhang, and Shiyu Chen. "Solving the Problem of Pressure Vessel with Constraint Conditions through Marine Predators Algorithm." Modern Economics & Management Forum 6, no. 1 (2025): 125. https://doi.org/10.32629/memf.v6i1.3568.

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With the development of artificial intelligence technology, various intelligent algorithms are becoming more and more mature. Swarm intelligent algorithm performs well among many algorithms. Optimization problems have been permeating into all aspects of life, and solving the condition optimization problem with constraints is a very important aspect. This paper adopts the newly proposed Marine Predators Algorithm in recent years. The original algorithm is improved by adding constraints. Optimization of pressure vessel problem using Marine predator optimization algorithm. The four variables of s
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Zheng, Lifang, Weixia Liu, and Hangying Chen. "Optimization of Patient Health Management Mechanism Under Intelligent Medical Information System." Journal of Medical Imaging and Health Informatics 12, no. 1 (2022): 83–91. http://dx.doi.org/10.1166/jmihi.2022.3782.

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The establishment of a scientific and complete intelligent medical information analysis application model is of great significance to promote the application of intelligent medical information. Aiming at the deficiencies of Artificial Fish School Algorithm (AFSA) in iterative convergence speed, low optimization accuracy, and Particle Swarm Optimization (PSO) algorithm easily falling into local extremes, this paper combines AFSA and PSO algorithms. We use the fast local convergence ability of the PSO algorithm to overcome the shortcomings of the AFSA algorithm’s low solution accuracy and slow c
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Zhang, Li Dong, Lei Jia, and Wen Xing Zhu. "A Review of some Intelligent Optimization Algorithms Applied to Intelligent Transportation System." Advanced Materials Research 383-390 (November 2011): 5717–23. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.5717.

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This paper attempts to summarize the findings of a large number of research papers concerning the application of intelligent optimization algorithms to ITS. A brief introduction to intelligence is included, for the benefit of readers unfamiliar with the techniques. Then it put emphasis on three kinds of intelligent optimization application in ITS, including ANN, GA and PSO. It should be noted first that each of the three subjects can prolong to a long paper, and second that there are also some other intelligent optimization method, such as fuzzy logic, ant colony, shuffle frog-leaping algorith
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Dissertations / Theses on the topic "Intelligent Optimization Algorithms"

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Al-Hajri, Muhammad T. "Electrical power energy optimization at hydrocarbon industrial plant using intelligent algorithms." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/12681.

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In this work, the potential of intelligent algorithms for optimizing the real power loss and enhancing the grid connection power factor in a real hydrocarbon facility electrical system is assessed. Namely, genetic algorithm (GA), improve strength Pareto evolutionary algorithm (SPEA2) and differential evolutionary algorithm (DEA) are developed and implemented. The economic impact associated with these objectives optimization is highlighted. The optimization of the subject objectives is addressed as single and multi-objective constrained nonlinear problems. Different generation modes and system
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Li, Futong. "Global Optimization Techniques Based on Swarm-intelligent and Gradient-free Algorithms." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42307.

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The need for solving nonlinear optimization problems is pervasive in many fields. Particle swarm optimization, advantageous with the simple underlying implementation logic, and simultaneous perturbation stochastic approximation, which is famous for its saving in the computational power with the gradient-free attribute, are two solutions that deserve attention. Many researchers have exploited their merits in widely challenging applications. However, there is a known fact that both of them suffer from a severe drawback, non- effectively converging to the global best solution, because of the loca
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Ranjeet, Tirtha. "Coevolutionary algorithms for the optimization of strategies for red teaming applications." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2012. https://ro.ecu.edu.au/theses/558.

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Red teaming (RT) is a process that assists an organization in finding vulnerabilities in a system whereby the organization itself takes on the role of an “attacker” to test the system. It is used in various domains including military operations. Traditionally, it is a manual process with some obvious weaknesses: it is expensive, time-consuming, and limited from the perspective of humans “thinking inside the box”. Automated RT is an approach that has the potential to overcome these weaknesses. In this approach both the red team (enemy forces) and blue team (friendly forces) are modelled as inte
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Alkindy, Bassam. "Combining approaches for predicting genomic evolution." Thesis, Besançon, 2015. http://www.theses.fr/2015BESA2012/document.

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En bio-informatique, comprendre comment les molécules d’ADN ont évolué au cours du temps reste un problème ouvert etcomplexe. Des algorithmes ont été proposés pour résoudre ce problème, mais ils se limitent soit à l’évolution d’un caractèredonné (par exemple, un nucléotide précis), ou se focalisent a contrario sur de gros génomes nucléaires (plusieurs milliardsde paires de base), ces derniers ayant connus de multiples événements de recombinaison – le problème étant NP completquand on considère l’ensemble de toutes les opérations possibles sur ces séquences, aucune solution n’existe à l’heureac
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Ahmad, Abdul-Rahim. "An Intelligent Expert System for Decision Analysis and Support in Multi-Attribute Layout Optimization." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/785.

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Layout Decision Analysis and Design is a ubiquitous problem in a variety of work domains that is important from both strategic and operational perspectives. It is largely a complex, vague, difficult, and ill-structured problem that requires intelligent and sophisticated decision analysis and design support. <br /><br /> Inadequate information availability, combinatorial complexity, subjective and uncertain preferences, and cognitive biases of decision makers often hamper the procurement of a superior layout configuration. Consequently, it is desirable to develop an intelligent decis
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Jung, Dongwon Jung. "Hierarchical Path Planning and Control of a Small Fixed-wing UAV: Theory and Experimental Validation." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19781.

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Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2008.<br>Committee Chair: Tsiotras, Panagiotis; Committee Member: Corban, Eric; Committee Member: Feron, Eric; Committee Member: Johnson, Eric; Committee Member: Vachtsevanos, George.
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Dezani, Henrique. "Otimização do fluxo do tráfego urbano modelado em redes de Petri utilizando algoritmo genético." [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260782.

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Orientador: Furio Damiani<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação<br>Made available in DSpace on 2018-08-21T16:34:47Z (GMT). No. of bitstreams: 1 Dezani_Henrique_D.pdf: 4299449 bytes, checksum: 739b593ddd96b9124b1882d5510a0ce0 (MD5) Previous issue date: 2012<br>Resumo: O controle do tráfego urbano constitui um importante tópico de pesquisa nos dias atuais. De fato, a cada dia aumenta a quantidade de veículos trafegando nas vias urbanas, as quais nem sempre são modificadas para a melhoria do tráfego. A existência de transportes
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Zaghdoud, Radhia. "Hybridation d’algorithme génétique pour les problèmes des véhicules intelligents autonomes : applications aux infrastructures portuaires de moyenne taille." Thesis, Ecole centrale de Lille, 2015. http://www.theses.fr/2015ECLI0023/document.

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L’objectif de ce travail est de développer un système d’affectation des conteneurs aux véhicules autonomes intelligents (AIVs) dans un terminal à conteneurs. Dans la première phase, on a développé un système statique pour résoudre le problème multi-objectif optimisant la durée totale des opérations de déplacement des conteneurs, le temps d’attente des véhicules aux niveaux de points de chargement et de déchargement et l’équilibre de temps de travail entre les véhicules. L’approche proposée est l’algorithme génétique(AG). Une extension de cette approche a été ensuite effectuée pour corriger les
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Roberts, Christopher. "Genetic algorithms for cluster optimization." Thesis, University of Birmingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368792.

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Kelman, Alexander. "Utilizing Swarm Intelligence Algorithms for Pathfinding in Games." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13636.

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The Ant Colony Optimization and Particle Swarm Optimization are two Swarm Intelligence algorithms often utilized for optimization. Swarm Intelligence relies on agents that possess fragmented knowledge, a concept not often utilized in games. The aim of this study is to research whether there are any benefits to using these Swarm Intelligence algorithms in comparison to standard algorithms such as A* for pathfinding in a game. Games often consist of dynamic environments with mobile agents, as such all experiments were conducted with dynamic destinations. Algorithms were measured on the length of
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Books on the topic "Intelligent Optimization Algorithms"

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Li, Kangshun, Yong Liu, and Wenxiang Wang, eds. Exploration of Novel Intelligent Optimization Algorithms. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4109-2.

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Melin, Patricia, and Oscar Castillo, eds. New Directions on Hybrid Intelligent Systems Based on Neural Networks, Fuzzy Logic, and Optimization Algorithms. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53713-4.

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Tao, Fei, Lin Zhang, and Yuanjun Laili. Configurable Intelligent Optimization Algorithm. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08840-2.

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Romania) BICS 2008 (2008 Tîrgu-Mureș. BICS 2008: Proceedings of the 1st International Conference, Bio-inspired Computational Methods Used for Solving Difficult Problems--Development of Intelligent and Complex Systems : Tirgu Mures, Romania, 5-7 November 2008. Edited by Enachescu Calin, Iantovics Barna L, and Filip F. G. American Institute of Physics, 2009.

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Koziel, Slawomir. Computational Optimization, Methods and Algorithms. Springer Berlin Heidelberg, 2011.

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Shi, Yuhui. Emerging research on swarm intelligence and algorithm optimization. Information Science Reference, 2015.

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Alba, Enrique. Metaheuristics for Dynamic Optimization. Springer Berlin Heidelberg, 2013.

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Ünal, Muhammet. Optimization of PID Controllers Using Ant Colony and Genetic Algorithms. Springer Berlin Heidelberg, 2013.

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Rao, R. Venkata. Mechanical Design Optimization Using Advanced Optimization Techniques. Springer London, 2012.

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Vasiljević, Darko. Classical and Evolutionary Algorithms in the Optimization of Optical Systems. Springer US, 2002.

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Book chapters on the topic "Intelligent Optimization Algorithms"

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Li, Changhe, Shoufei Han, Sanyou Zeng, and Shengxiang Yang. "Canonical Optimization Algorithms." In Intelligent Optimization. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3286-9_3.

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Gao, Yuehua, Peng Zhao, Lih-Sheng Turng, and Huamin Zhou. "Intelligent Optimization Algorithms." In Computer Modeling for Injection Molding. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118444887.ch10.

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Li, Changhe, Shoufei Han, Sanyou Zeng, and Shengxiang Yang. "Popular Evolutionary Computation Algorithms." In Intelligent Optimization. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3286-9_5.

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Li, Changhe, Shoufei Han, Sanyou Zeng, and Shengxiang Yang. "Basics of Evolutionary Computation Algorithms." In Intelligent Optimization. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3286-9_4.

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Rong, Hai-Jun, and Zhao-Xu Yang. "Optimization Algorithms." In Sequential Intelligent Dynamic System Modeling and Control. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1541-1_3.

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Wang, Gai-Ge, Xiaoqi Zhao, and Keqin Li. "Learning-Based Intelligent Optimization Algorithms." In Metaheuristic Algorithms. CRC Press, 2024. http://dx.doi.org/10.1201/9781003422426-4.

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Li, Lijuan, and Feng Liu. "Prospecting Swarm Intelligent Algorithms." In Adaptation, Learning, and Optimization. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20536-1_8.

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Li, Lijuan, and Feng Liu. "Introduction of Swarm Intelligent Algorithms." In Adaptation, Learning, and Optimization. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20536-1_1.

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Si, Tapas. "Swarm Programming Using Moth-Flame Optimization and Whale Optimization Algorithms." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4604-8_3.

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Zouggar, Souad Taleb, and Abdelkader Adla. "Optimization Techniques for Machine Learning." In Algorithms for Intelligent Systems. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0994-0_3.

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Conference papers on the topic "Intelligent Optimization Algorithms"

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V, Manojkumar, and R. Mahalakshmi. "Test Case Optimization Through Ant Colony Optimization Enabled Boosted Regression Model." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721935.

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Liu, Yang. "Intelligent Building Optimization Design Based on Artificial Neural Network with Tasmanian Devil Optimization." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721806.

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Chen, Shuangyan, Xinghua Wang, Haochen Shi, and Jingheng Zhang. "Intelligent Air Traffic Flow Optimization and Allocation Algorithms." In 2024 IEEE 7th International Conference on Information Systems and Computer Aided Education (ICISCAE). IEEE, 2024. https://doi.org/10.1109/iciscae62304.2024.10761629.

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Ye, Hankun, Mingyu Zhang, Guowei Lin, and Hong Wang. "Travel Route Planning Based on Intelligent Optimization Algorithms." In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2024. https://doi.org/10.1109/icicml63543.2024.10958097.

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Niu, Linlin. "Network Learning Optimization Strategies Driven by Intelligent Algorithms." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11070846.

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Ling, Huaizhi. "Utilization of Intelligent Optimization Algorithms in Geological Prospecting Seismic Data Analysis." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721913.

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Zhang, Chengjiang. "Research on Multi-Task Learning Optimization Models for Intelligent Driving." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11020470.

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Indukuri, Swetha Monica, Alok Kumar Singh, and D. Vijaya Kumar. "Power Management in Grid-Connected SPV-DSTATCOM Systems with Metaheuristic Optimization." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721985.

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Chintale, Pradeep, Rajesh Kumar Malviya, Narendra Babu Merla, Pavana Prasanthi Goli Chinna, Gopi Desaboyina, and Tharun Anand Reddy Sure. "Levy Flight Osprey Optimization Algorithm for Task Scheduling in Cloud Computing." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721633.

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R, Vinaykumar, Dhanush Kumar M K, Deekshith B H, and Kavita T K. "Design and Optimization of Ferrite-based Circulator for C-Band Application." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721682.

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Reports on the topic "Intelligent Optimization Algorithms"

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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
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Rinuado, Christina, William Leonard, Christopher Morey, Theresa Coumbe, Jaylen Hopson, and Robert Hilborn. Artificial intelligence (AI)–enabled wargaming agent training. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48419.

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Fiscal Year 2021 (FY21) work from the Engineer Research and Development Center Institute for Systems Engineering Research lever-aged deep reinforcement learning to develop intelligent systems (red team agents) capable of exhibiting credible behavior within a military course of action wargaming maritime framework infrastructure. Building from the FY21 research, this research effort sought to explore options to improve upon the wargaming framework infrastructure and to investigate opportunities to improve artificial intelligence (AI) agent behavior. Wargaming framework infrastructure enhancement
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Danylchuk, Hanna B., and Serhiy O. Semerikov. Advances in machine learning for the innovation economy: in the shadow of war. Криворізький державний педагогічний університет, 2023. http://dx.doi.org/10.31812/123456789/7732.

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This preface introduces the selected and revised papers presented at the 10th International Conference on Monitoring, Modeling &amp; Management of Emergent Economy (M3E2 2022), held online in Ukraine, on November 17-18, 2022. The conference aimed to bring together researchers, practitioners, and students from various fields to exchange ideas, share experiences, and discuss challenges and opportunities in applying computational intelligence and data science for the innovation economy. The innovation economy is a term that describes the emerging paradigm of economic development that is driven by
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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Ratmanski, Kiril, and Sergey Vecherin. Resilience in distributed sensor networks. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45680.

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With the advent of cheap and available sensors, there is a need for intelligent sensor selection and placement for various purposes. While previous research was focused on the most efficient sensor networks, we present a new mathematical framework for efficient and resilient sensor network installation. Specifically, in this work we formulate and solve a sensor selection and placement problem when network resilience is also a factor in the optimization problem. Our approach is based on the binary linear programming problem. The generic formulation is probabilistic and applicable to any sensor
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Shukla, Indu, Rajeev Agrawal, Kelly Ervin, and Jonathan Boone. AI on digital twin of facility captured by reality scans. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47850.

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The power of artificial intelligence (AI) coupled with optimization algorithms can be linked to data-rich digital twin models to perform predictive analysis to make better informed decisions about installation operations and quality of life for the warfighters. In the current research, we developed AI connected lifecycle building information models through the creation of a data informed smart digital twin of one of US Army Corps of Engineers (USACE) buildings as our test case. Digital twin (DT) technology involves creating a virtual representation of a physical entity. Digital twin is created
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RESEARCH ON DATA-DRIVEN INTELLIGENT DESIGN METHOD FOR ENERGY DISSIPATOR OF FLEXIBLE PROTECTION SYSTEMS. The Hong Kong Institute of Steel Construction, 2024. https://doi.org/10.18057/ijasc.2024.20.4.6.

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The brake ring, an essential buffer and energy dissipator within flexible protection systems for mitigating dynamic impacts from rockfall collapses, presents notable design challenges due to its significant deformation and strain characteristics. This study introduces a highly efficient and precise neural network model tailored for the design of brake rings, utilizing BP neural networks in conjunction with Particle Swarm Optimization (PSO) algorithms. The paper studies the key geometric parameters, including ring diameter, tube diameter, wall thickness, and aluminum sleeve length, with perform
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PERFORMANCE OPTIMIZATION OF A STEEL-UHPC COMPOSITE ORTHOTROPIC BRIDGE WITH INTELLIGENT ALGORITHM. The Hong Kong Institute of Steel Construction, 2022. http://dx.doi.org/10.18057/icass2020.p.160.

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To address the problems of pavement damage and fatigue cracking of orthotropic steel deck (OSD) in bridges, an innovative composite bridge deck composed of OSD with open ribs and ultra-high performance concrete (UHPC) layer was proposed. Firstly, the stress responses of fatigue-prone details in the composite bridge deck were investigated by refined two-scale finite element analysis. The results show that the rib-to-deck joint can achieve an infinite fatigue life, while the floorbeam detail of rib-tofloorbeam joint indicates finite fatigue life. Then, response surface models of stress ranges of
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