Relevant bibliographies by topics / Intelligent optimization techniques

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

Academic literature on the topic 'Intelligent optimization techniques'

Author: Grafiati
Published: 4 June 2025
Last updated: 6 July 2025

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Journal articles on the topic "Intelligent optimization techniques"

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Ahmet, Demir, and Kose Utku. "BRAIN Journal - Solving Optimization Problems via Vortex Optimization Algorithm and Cognitive Development Optimization Algorithm." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 7, no. 4 (2016): 23–42. https://doi.org/10.5281/zenodo.1045013.

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ABSTRACT In the fields which require finding the most appropriate value, optimization became a vital approach to employ effective solutions. With the use of optimization techniques, many different fields in the modern life have found solutions to their real-world based problems. In this context, classical optimization techniques have had an important popularity. But after a while, more advanced optimization problems required the use of more effective techniques. At this point, Computer Science took an important role on providing software related techniques to improve the associated literature. Today, intelligent optimization techniques based on Artificial Intelligence are widely used for optimization problems. The objective of this paper is to provide a comparative study on the employment of classical optimization solutions and Artificial Intelligence solutions for enabling readers to have idea about the potential of intelligent optimization techniques. At this point, two recently developed intelligent optimization algorithms, Vortex Optimization Algorithm (VOA) and Cognitive Development Optimization Algorithm (CoDOA), have been used to solve some multidisciplinary optimization problems provided in the source book Thomas' Calculus 11th Edition and the obtained results have compared with classical optimization solutions.
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Aashish, Kumar Bohre, Ganga Agnihotri Dr., and Manisha Dubey Dr. "THE BUTTERFLY-PARTICLE SWARM OPTIMIZATION (BUTTERFLY-PSO/BF-PSO) TECHNIQUE AND ITS VARIABLES." International Journal of Soft Computing, Mathematics and Control (IJSCMC) 4, no. 3 (2022): 17. https://doi.org/10.5281/zenodo.6719817.

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The new presented Butterfly-PSO technique (or BF-PSO) is basically originated by Particle Swarm Optimization (PSO). The Butterfly-PSO technique (BF-PSO) appears as a new growing star among all optimization techniques. The proposed ‘Butterfly- Particle Swarm Optimization (Butterfly or BF-PSO)’ is inspired by butterfly natural intelligence, character, behavior, intelligent network and intelligent communication during the nectar search process. The BF-PSO introduces new parameters such as sensitivity of butterfly (s), probability of food (nectar) (p), the degree of the node and the time varying probability coefficient (α). These parameters improve the searching ability, excellent convergence and the overall performance of the Butterfly-PSO effectivly. The BF-PSO optimizations results have been presented for various functions with the multi-dimension problems.
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Aashish, Kumar Bohre, Agnihotri Ganga, and Dubey Manisha. "The Butterfly-Particle Swarm Optimization (Butterfly-PSO/BF-PSO) Technique and Its Variables." International Journal of Soft Computing, Mathematics and Control (IJSCMC) 4, no. 3 (2015): 23 to 39. https://doi.org/10.5281/zenodo.3632366.

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The new presented Butterfly-PSO technique (or BF-PSO) is basically originated by Particle Swarm Optimization (PSO). The Butterfly-PSO technique (BF-PSO) appears as a new growing star among all optimization techniques. The proposed 'Butterfly- Particle Swarm Optimization (Butterfly or BF-PSO)' is inspired by butterfly natural intelligence, character, behavior, intelligent network and intelligent communication during the nectar search process. The BF-PSO introduces new parameters such as sensitivity of butterfly (s), probability of food (nectar) (p), the degree of the node and the time varying probability coefficient (a). These parameters improve the searching ability, excellent convergence and the overall performance of the Butterfly-PSO effectivly. The BF-PSO optimizations results have been presented for various functions with the multi-dimension problems.
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Calp, M. Hanefi. "Evaluation of Multidisciplinary Effects of Artificial Intelligence with Optimization Perspective." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 10, no. 1 (2019): 20. https://doi.org/10.70594/brain/v10.i1/2.

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Artificial Intelligence has an important place in the scientific community as a result of its successful outputs in terms of different fields. In time, the field of Artificial Intelligence has been divided into many sub-fields because of increasing number of different solution approaches, methods, and techniques. Machine Learning has the most remarkable role with its functions to learn from samples from the environment. On the other hand, intelligent optimization done by inspiring from nature and swarms had its own unique scientific literature, with effective solutions provided for optimization problems from different fields. Because intelligent optimization can be applied in different fields effectively, this study aims to provide a general discussion on multidisciplinary effects of Artificial Intelligence by considering its optimization oriented solutions. The study briefly focuses on background of the intelligent optimization briefly and then gives application examples of intelligent optimization from a multidisciplinary perspective.
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Matthews, J. "Book Review: Manufacturing Optimization Through Intelligent Techniques." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 221, no. 4 (2007): 759. http://dx.doi.org/10.1177/095440540722100401.

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Ojekudo, Okardi, Biobele, and Nathaniel Akofure. "ANALYSIS OF MODERN TECHNIQUES FOR SOFTWARE OPTIMIZATION." International Journal of Computer Science and Mobile Computing 10, no. 7 (2021): 46–55. http://dx.doi.org/10.47760/ijcsmc.2021.v10i07.007.

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Traditional Methods of optimization have failed to meet up the rapid changing world in the demand of high quality and accuracy in solution delivery. Optimization literally means looking for the best possible or most desired solution to a problem. Optimization techniques are basically classified into three groups, namely; the Traditional Method, Artificial Intelligent Method, and Hybrid Artificial Intelligent technique. In this paper, an attempt is made to review literatures on different modern optimization techniques for application in various disciplines. A general review was made on some of the modern optimization methods such as Genetic Algorithm, Ant colony method, Honey Bee optimization method, and Simulated Annealing optimization.
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VASYLKIVSKYI, Mikola, Andrii PRYKMETA, Andrii OLIYNYK, and Diana NIKITOVYCH. "OPTIMIZATION OF INTELLIGENT TELECOMMUNICATION NETWORKS." Herald of Khmelnytskyi National University. Technical sciences 217, no. 1 (2023): 33–41. http://dx.doi.org/10.31891/2307-5732-2023-317-1-33-41.

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The paper presents the results of research on the use of machine learning in telecommunication networks and describes the basics of the theory of artificial intelligence. The impact of dynamic Bayesian network (DBN) and DNN on the development of many technologies, including user activity detection, channel estimation, and mobility tracking, is determined. The indicators of the effectiveness of communications based on the theory of information bottlenecks, which is at the junction of machine learning and forecasting, statistics and information theory, are considered. A neural network model that is pretrained for high-level tasks and divided into transmitter-side and receiver-side uses is investigated. The process of learning the model, which is performed after its adjustment, taking ino account the existing transmission channels, is considered. New ANN learning techniques capable of predicting or adapting to sudden changes in a wireless network, such as federated learning and multiagent reinforcement learning (MARL), are reviewed. The DBN model, which describes a system that dynamically changes or develops over time, is studied. The considered model provides constant monitoring of work and updating of the system and prediction of its behavior. Distributed forecasting of channel states and user locations as a key component in the development of reliable wireless communication systems is studied. The possibility of increasing the number of degrees of freedom of the generalized wireless channel G(E) in terms of: the physical propagation channel, the directional diagram of the antenna array and mutual influence, electromagnetic physical characteristics is substantiated. The impact of ultra-highresolution theory on the development of many technologies, including localization algorithms, compressed sampling, and wireless imaging algorithms, is also identified. Mathematical expressions for optimizing the functional characteristics of 5G/6G radio networks are presented using new, sufficiently formal and at the same time universal mathematical tools with an emphasis on deep learning technologies, which allow systematic, reliable and interpretable analysis of large random networks and a wide range of their network models and practical networks.
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Phommixay, Sengthavy, Mamadou Lamine Doumbia, and David Lupien St-Pierre. "Review on the cost optimization of microgrids via particle swarm optimization." International Journal of Energy and Environmental Engineering 11, no. 1 (2019): 73–89. http://dx.doi.org/10.1007/s40095-019-00332-1.

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AbstractEconomic analysis is an important tool in evaluating the performances of microgrid (MG) operations and sizing. Optimization techniques are required for operating and sizing an MG as economically as possible. Various optimization approaches are applied to MGs, which include classic and artificial intelligence techniques. Particle swarm optimization (PSO) is one of the most frequently used methods for cost optimization due to its high performance and flexibility. PSO has various versions and can be combined with other intelligent methods to realize improved performance optimization. This paper reviews the cost minimization performances of various economic models that are based on PSO with regard to MG operations and sizing. First, PSO is described, and its performance is analyzed. Second, various objective functions, constraints and cost functions that are used in MG optimizations are presented. Then, various applications of PSO for MG sizing and operations are reviewed. Additionally, optimal operation costs that are related to the energy management strategy, unit commitment, economic dispatch and optimal power flow are investigated.
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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 algorithm et.al.. On the constraint of time and paper volume, we only analyzed those three algorithms, their state-and-the-art use in ITS, and their future development trend.
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Feng, Biqian, Junyuan Gao, Yongpeng Wu, Wenjun Zhang, Xiang-Gen Xia, and Chengshan Xiao. "Optimization Techniques in Reconfigurable Intelligent Surface Aided Networks." IEEE Wireless Communications 28, no. 6 (2021): 87–93. http://dx.doi.org/10.1109/mwc.001.2100196.

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Dissertations / Theses on the topic "Intelligent optimization techniques"

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Ngatchou, Patrick. "Intelligent techniques for optimization and estimation /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/5827.

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Mohamed, Abdelhamed. "Optimization Techniques for Reconfigurable Intelligent Surfaces Assisted Wireless Networks." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST137.

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Récemment, l'émergence des surfaces intelligentes reconfigurables (RIS) a suscité une vive attention de l'industrie et du monde universitaire. Un RIS est une surface plane constituée d'un grand nombre d'éléments réfléchissants passifs à faible coût. En ajustant soigneusement les déphasages des éléments réfléchissants, un RIS peut remodeler l'environnement sans fil pour une meilleure communication. En général, cette thèse fournit des contributions sur : (i) les performances des RIS basées sur des modèles de rayonnement électromagnétique précis et réalistes. De plus, elle fournit des cadres d'optimisation pour améliorer la performance des systèmes de communication dans les deux cas d'utilisation suivants : (i) Améliorer conjointement le taux d'information et la quantité de puissance récoltée dans un réseau sans fil multi-utilisateurs MISO descendant assisté par les RIS. (ii) améliorer l'efficacité spectrale pour un grand nombre d'utilisateurs situés en bordure de cellule ou de l'autre côté du RIS en utilisant des omni-surfaces intelligentes (IOS). Le chapitre 1 présente les défis à relever pour répondre aux exigences des réseaux 6G, le concept d'environnements radio intelligents et les RIS, qui constituent l'une des technologies habilitantes. Dans les communications futures, les RIS sont une technique clé qui aura des applications potentielles permettant d'obtenir une connectivité sans faille tout en consommant moins d'énergie. Le chapitre 2 présente les systèmes de communication assistés par RIS. Le principe de réflexion, le problème d'estimation de canal et le problème de conception du système sont présentés en détail. Les recherches de pointe sur les problèmes d'estimation de canal et de conception de système sont passées en revue. Le chapitre 3 étudie l'impact de modèles de reradiation réalistes pour les RIS en fonction de l'inter-distance sub-longueur d'onde entre les éléments proches du RIS, les niveaux de quantification des coefficients de réflexion, l'interaction entre l'amplitude et la phase des coefficients de réflexion, et la présence d'interférences électromagnétiques. En conclusion, notre étude montre que, en raison de contraintes de conception, telles que la nécessité d'utiliser des coefficients de réflexion quantifiés ou l'interaction inhérente entre la phase et l'amplitude des coefficients de réflexion, un RIS peut reradir la puissance vers des directions non désirées qui dépendent des ondes électromagnétiques prévues et interférentes. Le chapitre 4 aborde le problème de l'optimisation simultanée du taux d'information et de la puissance récoltée dans un réseau sans fil multi-utilisateurs MISO en liaison descendante avec surface intelligente reconfigurable (RIS) et transfert simultané d'information et de puissance sans fil (SWIPT). Un algorithme pratique est développé par une interaction entre l'optimisation alternée, l'optimisation séquentielle et les méthodes basées sur les prix. Le chapitre 5 propose un algorithme d'optimisation qui a un taux de convergence rapide en quelques itérations pour maximiser le taux de somme dans les canaux de diffusion MIMO assistés par IOS, qui peut être exploité pour servir l'utilisateur de bord de cellule et améliorer la couverture du réseau. La particularité de ce travail est de considérer que les coefficients de réflexion et de transmission d'un IOS sont étroitement couplés. Enfin, le chapitre 6 résume les principales conclusions de la thèse et discute des orientations futures possibles qui méritent d'être étudiées pour libérer tout le potentiel des RIS et les mettre en pratique<br>Recently, the emergence of reconfigurable intelligent surface (RIS) has attracted heated attention from both industry and academia. An RIS is a planar surface that consists of a large number of low-cost passive reflecting elements. By carefully adjusting the phase shifts of the reflecting elements, an RIS can reshape the wireless environment for better communication. In general, this thesis provides contributions on: (i) the performance of RISs based on accurate and realistic electromagnetic reradiation models. Moreover, it provides some of optimization frameworks for enhancing the communication system performance on the following two use case: (i) To jointly improves the information rate and the amount of harvested power in a RIS-aided MISO downlink multiuser wireless network. (ii) enhancing spectral efficiency for large number of users located on cell edge or on the other side of the RIS by utilizing the intelligent omni-surfaces (IOSs).Chapter 1 introduces the challenges of fulfilling the requirements of of 6G networks, the concept of smart radio environments and RIS as it is one of the enabling technologies. In future communications, RIS is a key technique that will have potential applications which will achieve seamless connectivity and less energy consumption at the same time. Chapter 2 also introduces the state-of-art optimization techniques developed for RIS-aided systems. Firstly, it introduces the system models of RIS-aided MIMO systems and then investigates the reflection principle of RISs. In addition, it introduces the Optimization techniques challenges of RIS-assisted systems. Also, the proposed optimization techniques for designing the continuous and discrete phase shifts are presented in detail. Chapter 3 studies the impact of realistic reradiation models for RISs as a function of the sub-wavelength inter-distance between nearby elements of the RIS, the quantization levels of the reflection coefficients, the interplay between the amplitude and phase of the reflection coefficients, and the presence of electromagnetic interference. In conclusion, our study shows that, due to design constraints, such as the need to use quantized reflection coefficients or the inherent interplay between the phase and the amplitude of the reflection coefficients, a RIS may reradiate power towards unwanted directions that depend on the intended and interfering electromagnetic waves. Chapter 4 considers the problem of simultaneously optimizing the information rate and the harvested power in a reconfigurable intelligent surface (RIS)-aided MISO downlink multiuser wireless network with simultaneous wireless information, and power transfer (SWIPT) is addressed. A practical algorithm is developed through an interplay of alternating optimization, sequential optimization, and pricing-based methods. Chapter 5 proposes an optimization algorithm that has a rapid convergence rate in a few iterations for maximizing the sum rate in IOS-aided MIMO broadcast channels, which can be exploited to serve the cell-edge user and enhance network coverage. This work's distinguishable feature lies in considering that the reflection and transmission coefficients of an IOS are tightly coupled. Finally, Chapter 6 summarizes the main findings of the thesis and discusses possible future directions that are worth investigating to unlock the full potential of RIS and bring it into practice
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NETO, OMAR PARANAIBA VILELA. "DESIGN, OPTIMIZATION, SIMULATION AND PREDICTION OF NANOSTRUCTURES PROPERTIES BY COMPUTATIONAL INTELLIGENCE TECHNIQUES: INTELLIGENT COMPUTATIONAL NANOTECHNOLOGY." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=15182@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO<br>Esta tese investiga a Nanotecnologia Computacional Inteligente, isto é, o apoio de técnicas de Inteligência Computacional (IC) nos desafios enfrentados pela Nanociência e Nanotecnologia. Por exemplo, utilizam-se as Redes Neurais para construir sistemas de inferência capazes de relacionar um conjunto de parâmetros de entrada com as características finais das nanoestruturas, permitindo aos pesquisadores prever o comportamento de outras nanoestruturas ainda não realizadas experimentalmente. A partir dos sistemas de inferência, Algoritmos Genéticos são então empregados com o intuito de encontrar o conjunto ótimo de parâmetros de entrada para a síntese (projeto) de uma nanoestrutura desejada. Numa outra linha de investigação, os Algoritmos Genéticos são usados para a otimização de parâmetros de funções de base para cálculos ab initio. Neste caso, são otimizados os expoentes das funções gaussianas que compõem as funções de base. Em outra abordagem, os Algoritmos Genéticos são aplicados na otimização de agregados atômicos e moleculares, permitindo aos pesquisadores estudar teoricamente os agregados formados experimentalmente. Por fim, o uso destes algoritmos, aliado ao uso de simuladores, é aplicado na síntese automática de OLEDs e circuitos de Autômatos Celulares com Pontos Quânticos (QCA). Esta pesquisa revelou o potencial da IC em aplicações inovadoras. Os sistemas híbridos de otimização e inferência, por exemplo, concebidos para prever a altura, a densidade e o desvio padrão de pontos quânticos auto-organizáveis, apresentam altos níveis de correlação com os resultados experimentais e baixos erros percentuais (inferior a 10%). O módulo de elasticidade de nanocompósitos também é previsto por um sistema semelhante e apresenta erros percentuais ainda menores, entorno de 4%. Os Algoritmos Genéticos, juntamente com o software de modelagem molecular Gaussian03, otimizam os parâmetros de funções que geram expoentes de primitivas gaussianas de funções de base para cálculos hartree-fock, obtendo energias menores do que aquelas apresentadas nas referencias. Em outra aplicação, os Algoritmos Genéticos também se mostram eficientes na busca pelas geometrias de baixa energia dos agregados atômicos de (LiF)nLi+, (LiF)n e (LiF)nF-, obtendo uma série de novos isômeros ainda não propostos na literatura. Uma metodologia semelhante é aplicada em um sistema inédito para entender a formação de agregados moleculares de H2O iônicos, partindo-se de agregados neutros. Os resultados mostram como os agregados podem ser obtidos a partir de diferentes perspectivas, formando estruturas ainda não investigadas na área científica. Este trabalho também apresenta a síntese automática de circuitos de QCA robustos. Os circuitos obtidos apresentam grau de polarização semelhante àqueles propostos pelos especialistas, mas com uma importante redução na quantidade de células. Por fim, um sistema envolvendo Algoritmos Genéticos e um modelo analítico de OLEDs multicamadas otimizam as concentrações de materiais orgânicos em cada camada com o intuito de obter dispositivos mais eficientes. Os resultados revelam um dispositivo 9,7% melhor que a solução encontrada na literatura, sendo estes resultados comprovados experimentalmente. Em resumo, os resultados da pesquisa permitem constatar que a inédita integração das técnicas de Inteligência Computacional com Nanotecnologia Computacional, aqui denominada Nanotecnologia Computacional Inteligente, desponta como uma promissora alternativa para acelerar as pesquisas em Nanociência e o desenvolvimento de aplicações nanotecnológicas.<br>This thesis investigates the Intelligent Computational Nanotechnology, that is, the support of Computational Intelligence (CI) techniques in the challenges faced by the Nanoscience and Nanotechnology. For example, Neural Networks are used for build Inference systems able to relate a set of input parameters with the final characteristics of the nanostructures, allowing the researchers foresees the behavior of other nanostructures not yet realized experimentally. From the inference systems, Genetic Algorithms are then employees with the intention of find the best set of input parameters for the synthesis (project) of a desired nanostructure. In another line of inquiry, the Genetic Algorithms are used for the base functions optimization used in ab initio calculations. In that case, the exponents of the Gaussian functions that compose the base functions are optimized. In another approach, the Genetic Algorithms are applied in the optimization of molecular and atomic clusters, allowing the researchers to theoretically study the experimentally formed clusters. Finally, the use of these algorithms, use together with simulators, is applied in the automatic synthesis of OLEDs and circuits of Quantum Dots Cellular Automata (QCA). This research revealed the potential of the CI in innovative applications. The hybrid systems of optimization and inference, for example, conceived to foresee the height, the density and the height deviation of self-assembled quantum dots, present high levels of correlation with the experimental results and low percentage errors (lower to 10%). The Young’s module of nanocomposites is also predicted by a similar system and presents percentage errors even smaller, around 4%. The Genetic Algorithms, jointly with the package of molecular modeling Gaussian03, optimize the parameters of functions that generate exponents of primitive Gaussian functions of base sets for hartree-fock calculations, obtaining smaller energies than those presented in the literature. In another application, the Genetic Algorithms are also efficient in the search by the low energy geometries of the atomic clusters of (LiF) nLi +, (LiF) n and (LiF) nF-, obtaining a set of new isomers yet not propose in the literature. A similar methodology is applied in an unpublished system for understand the formation of molecular cluster of ionic H2O from neutral clusters. The results show how the clusters can be obtained from different perspectives, forming structures not yet investigate in the scientific area. This work also presents the automatic synthesis of robust QCA circuits. The circuits obtained present high polarization, similar to those proposed by the specialists, but with an important reduction in the quantity of cells. Finally, a system involving Genetic Algorithms and an analytic model of multilayer OLEDs optimize the concentrations of organic material in each layer in order to obtain more efficient devices. The results reveal a device 9.7% better that the solution found in the literature, being these results verified experimentally. In summary, the results of the proposed research allow observe that the unpublished integration of the techniques of Computational Intelligence with Computational Nanotechnology, here named Intelligent Computational Nanotechnology, emerges as a promising alternative for accelerate the researches in Nanoscince and the development of application in Nanotechnology.
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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 local “traps” spreading on the searching space. In this article, we propose two approaches to remedy this issue by combined their advantages. In the first algorithm, the gradient information helps optimize half of the particles at the initialization stage and then further updates the global best position. If the global best position is located in one of the local optima, the searching surface’s additional gradient estimation can help it jump out. The second algorithm expands the implementation of the gradient information to all the particles in the swarm to obtain the optimized personal best position. Both have to obey the rule created for updating the particle(s); that is, the solution found after employing the gradient information to the particle(s) has to perform more optimally. In this work, the experiments include five cases. The three previous methods with a similar theoretical basis and the two basic algorithms take participants in all five. The experimental results prove that the proposed two algorithms effectively improved the basic algorithms and even outperformed the previously designed three algorithms in some scenarios.
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Brka, Adel. "Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2015. https://ro.ecu.edu.au/theses/1756.

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Wind and solar irradiance are promising renewable alternatives to fossil fuels due to their availability and topological advantages for local power generation. However, their intermittent and unpredictable nature limits their integration into energy markets. Fortunately, these disadvantages can be partially overcome by using them in combination with energy storage and back-up units. However, the increased complexity of such systems relative to single energy systems makes an optimal sizing method and appropriate Power Management Strategy (PMS) research priorities. This thesis contributes to the design and integration of stand-alone hybrid renewable energy systems by proposing methodologies to optimise the sizing and operation of hydrogen-based systems. These include using intelligent techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Neural Networks (NNs). Three design aspects: component sizing, renewables forecasting, and operation coordination, have been investigated. The thesis includes a series of four journal articles. The first article introduced a multi-objective sizing methodology to optimise standalone, hydrogen-based systems using GA. The sizing method was developed to calculate the optimum capacities of system components that underpin appropriate compromise between investment, renewables penetration and environmental footprint. The system reliability was assessed using the Loss of Power Supply Probability (LPSP) for which a novel modification was introduced to account for load losses during transient start-up times for the back-ups. The second article investigated the factors that may influence the accuracy of NNs when applied to forecasting short-term renewable energy. That study involved two NNs: Feedforward, and Radial Basis Function in an investigation of the effect of the type, span and resolution of training data, and the length of training pattern, on shortterm wind speed prediction accuracy. The impact of forecasting error on estimating the available wind power was also evaluated for a commercially available wind turbine. The third article experimentally validated the concept of a NN-based (predictive) PMS. A lab-scale (stand-alone) hybrid energy system, which consisted of: an emulated renewable power source, battery bank, and hydrogen fuel cell coupled with metal hydride storage, satisfied the dynamic load demand. The overall power flow of the constructed system was controlled by a NN-based PMS which was implemented using MATLAB and LabVIEW software. The effects of several control parameters, which are either hardware dependent or affect the predictive algorithm, on system performance was investigated under the predictive PMS, this was benchmarked against a rulebased (non-intelligent) strategy. The fourth article investigated the potential impact of NN-based PMS on the economic and operational characteristics of such hybrid systems. That study benchmarked a rule-based PMS to its (predictive) counterpart. In addition, the effect of real-time fuel cell optimisation using PSO, when applied in the context of predictive PMS was also investigated. The comparative analysis was based on deriving the cost of energy, life cycle emissions, renewables penetration, and duty cycles of fuel cell and electrolyser units. The effects of other parameters such the LPSP level, prediction accuracy were also investigated. The developed techniques outperformed traditional approaches by drawing upon complex artificial intelligence models. The research could underpin cost-effective, reliable power supplies to remote communities as well as reducing the dependence on fossil fuels and the associated environmental footprint.
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Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.

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S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.
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Hernández, Pibernat Hugo. "Swarm intelligence techniques for optimization and management tasks insensor networks." Doctoral thesis, Universitat Politècnica de Catalunya, 2012. http://hdl.handle.net/10803/81861.

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The main contributions of this thesis are located in the domain of wireless sensor netorks. More in detail, we introduce energyaware algorithms and protocols in the context of the following topics: self-synchronized duty-cycling in networks with energy harvesting capabilities, distributed graph coloring and minimum energy broadcasting with realistic antennas. In the following, we review the research conducted in each case. We propose a self-synchronized duty-cycling mechanism for sensor networks. This mechanism is based on the working and resting phases of natural ant colonies, which show self-synchronized activity phases. The main goal of duty-cycling methods is to save energy by efficiently alternating between different states. In the case at hand, we considered two different states: the sleep state, where communications are not possible and energy consumption is low; and the active state, where communication result in a higher energy consumption. In order to test the model, we conducted an extensive experimentation with synchronous simulations on mobile networks and static networks, and also considering asynchronous networks. Later, we extended this work by assuming a broader point of view and including a comprehensive study of the parameters. In addition, thanks to a collaboration with the Technical University of Braunschweig, we were able to test our algorithm in the real sensor network simulator Shawn (http://shawn.sf.net). The second part of this thesis is devoted to the desynchronization of wireless sensor nodes and its application to the distributed graph coloring problem. In particular, our research is inspired by the calling behavior of Japanese tree frogs, whose males use their calls to attract females. Interestingly, as female frogs are only able to correctly localize the male frogs when their calls are not too close in time, groups of males that are located nearby each other desynchronize their calls. Based on a model of this behavior from the literature, we propose a novel algorithm with applications to the field of sensor networks. More in detail, we analyzed the ability of the algorithm to desynchronize neighboring nodes. Furthermore, we considered extensions of the original model, hereby improving its desynchronization capabilities.To illustrate the potential benefits of desynchronized networks, we then focused on distributed graph coloring. Later, we analyzed the algorithm more extensively and show its performance on a larger set of benchmark instances. The classical minimum energy broadcast (MEB) problem in wireless ad hoc networks, which is well-studied in the scientific literature, considers an antenna model that allows the adjustment of the transmission power to any desired real value from zero up to the maximum transmission power level. However, when specifically considering sensor networks, a look at the currently available hardware shows that this antenna model is not very realistic. In this work we re-formulate the MEB problem for an antenna model that is realistic for sensor networks. In this antenna model transmission power levels are chosen from a finite set of possible ones. A further contribution concerns the adaptation of an ant colony optimization algorithm --currently being the state of the art for the classical MEB problem-- to the more realistic problem version, the so-called minimum energy broadcast problem with realistic antennas (MEBRA). The obtained results show that the advantage of ant colony optimization over classical heuristics even grows when the number of possible transmission power levels decreases. Finally we build a distributed version of the algorithm, which also compares quite favorably against centralized heuristics from the literature.<br>Las principles contribuciones de esta tesis se encuentran en el domino de las redes de sensores inalámbricas. Más en detalle, introducimos algoritmos y protocolos que intentan minimizar el consumo energético para los siguientes problemas: gestión autosincronizada de encendido y apagado de sensores con capacidad para obtener energía del ambiente, coloreado de grafos distribuido y broadcasting de consumo mínimo en entornos con antenas reales. En primer lugar, proponemos un sistema capaz de autosincronizar los ciclos de encendido y apagado de los nodos de una red de sensores. El mecanismo está basado en las fases de trabajo y reposo de las colonias de hormigas tal y como estas pueden observarse en la naturaleza, es decir, con fases de actividad autosincronizadas. El principal objectivo de este tipo de técnicas es ahorrar energía gracias a alternar estados de forma eficiente. En este caso en concreto, consideramos dos estados diferentes: el estado dormido, en el que los nodos no pueden comunicarse y el consumo energético es bajo; y el estado activo, en el que las comunicaciones propician un consumo energético elevado. Con el objetivo de probar el modelo, se ha llevado a cabo una extensa experimentación que incluye tanto simulaciones síncronas en redes móviles y estáticas, como simulaciones en redes asíncronas. Además, este trabajo se extendió asumiendo un punto de vista más amplio e incluyendo un detallado estudio de los parámetros del algoritmo. Finalmente, gracias a la colaboración con la Technical University of Braunschweig, tuvimos la oportunidad de probar el mecanismo en el simulador realista de redes de sensores, Shawn (http://shawn.sf.net). La segunda parte de esta tesis está dedicada a la desincronización de nodos en redes de sensores y a su aplicación al problema del coloreado de grafos de forma distribuida. En particular, nuestra investigación está inspirada por el canto de las ranas de árbol japonesas, cuyos machos utilizan su canto para atraer a las hembras. Resulta interesante que debido a que las hembras solo son capaces de localizar las ranas macho cuando sus cantos no están demasiado cerca en el tiempo, los grupos de machos que se hallan en una misma región desincronizan sus cantos. Basado en un modelo de este comportamiento que se encuentra en la literatura, proponemos un nuevo algoritmo con aplicaciones al campo de las redes de sensores. Más en detalle, analizamos la habilidad del algoritmo para desincronizar nodos vecinos. Además, consideramos extensiones del modelo original, mejorando su capacidad de desincronización. Para ilustrar los potenciales beneficios de las redes desincronizadas, nos centramos en el problema del coloreado de grafos distribuido que tiene relación con diferentes tareas habituales en redes de sensores. El clásico problema del broadcasting de consumo mínimo en redes ad hoc ha sido bien estudiado en la literatura. El problema considera un modelo de antena que permite transmitir a cualquier potencia elegida (hasta un máximo establecido por el dispositivo). Sin embargo, cuando se trabaja de forma específica con redes de sensores, un vistazo al hardware actualmente disponible muestra que este modelo de antena no es demasiado realista. En este trabajo reformulamos el problema para el modelo de antena más habitual en redes de sensores. En este modelo, los niveles de potencia de transmisión se eligen de un conjunto finito de posibilidades. La siguiente contribución consiste en en la adaptación de un algoritmo de optimización por colonias de hormigas a la versión más realista del problema, también conocida como broadcasting de consumo mínimo con antenas realistas. Los resultados obtenidos muestran que la ventaja de este método sobre heurísticas clásicas incluso crece cuando el número de posibles potencias de transmisión decrece. Además, se ha presentado una versión distribuida del algoritmo, que también se compara de forma bastante favorable contra las heurísticas centralizadas conocidas.
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Turan, Kamil Hakan. "Reliability-based Optimization Of River Bridges Using Artificial Intelligence Techniques." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613062/index.pdf.

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Proper bridge design is based on consideration of structural, hydraulic, and geotechnical conformities at an optimum level. The objective of this study is to develop an optimization-based methodology to select appropriate dimensions for components of a river bridge such that the aforementioned design aspects can be satisfied jointly. The structural and geotechnical design parts uses a statisticallybased technique, artificial neural network (ANN) models. Therefore, relevant data of many bridge projects were collected and analyzed from different aspects to put them into a matrix form. ANN architectures are used in the objective function of the optimization problem, which is modeled using Genetic Algorithms with penalty functions as constraint handling method. Bridge scouring reliability comprises one of the constraints, which is performed using Monte-Carlo Simulation technique. All these mechanisms are assembled in a software framework, named as AIROB. Finally, an application built on AIROB is presented to assess the outputs of the software by focusing on the evaluations of hydraulic &ndash<br>structure interactions.
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Wilke, Daniel N. "Analysis of the particle swarm optimization algorithm." Pretoria : [s.n.], 2005. http://upetd.up.ac.za/thesis/available/etd-01312006-125743.

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Ibn, Khedher Hatem. "Optimization and virtualization techniques adapted to networking." Thesis, Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0007/document.

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Dans cette thèse, on présente nos travaux sur la virtualisation dans le contexte de la réplication des serveurs de contenu vidéo. Les travaux couvrent la conception d’une architecture de virtualisation pour laquelle on présente aussi plusieurs algorithmes qui peuvent réduire les couts globaux à long terme et améliorer la performance du système. Le travail est divisé en plusieurs parties : les solutions optimales, les solutions heuristiques pour des raisons de passage à l’échelle, l’orchestration des services, l’optimisation multi-objective, la planification de services dans des réseaux actifs et complexes et l'intégration d'algorithmes dans une plate-forme réelle<br>In this thesis, we designed and implemented a tool which performs optimizations that reduce the number of migrations necessary for a delivery task. We present our work on virtualization in the context of replication of video content servers. The work covers the design of a virtualization architecture for which there are also several algorithms that can reduce overall costs and improve system performance. The thesis is divided into several parts: optimal solutions, greedy (heuristic) solutions for reasons of scalability, orchestration of services, multi-objective optimization, service planning in complex active networks, and integration of algorithms in real platform. This thesis is supported by models, implementations and simulations which provide results that showcase our work, quantify the importance of evaluating optimization techniques and analyze the trade-off between reducing operator cost and enhancing end user satisfaction index
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Books on the topic "Intelligent optimization techniques"

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Venkata Rao, R., and Jan Taler, eds. Advanced Engineering Optimization Through Intelligent Techniques. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-8196-6.

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Venkata Rao, Ravipudi, and Jan Taler, eds. Advanced Engineering Optimization Through Intelligent Techniques. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9285-8.

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Venkata Rao, Ravipudi, and Jan Taler, eds. Advanced Engineering Optimization Through Intelligent Techniques. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4654-5.

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Köppen, Mario. Intelligent Computational Optimization in Engineering: Techniques and Applications. Springer-Verlag Berlin Heidelberg, 2011.

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Labbadi, Moussa, Kamal Elyaalaoui, Loubna Bousselamti, Mohammed Ouassaid, and Mohamed Cherkaoui. Modeling, Optimization and Intelligent Control Techniques in Renewable Energy Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98737-4.

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Chen, Shu-Heng. Multi-agent applications with evolutionary computation and biologically inspired technologies: Intelligent techniques for ubiquity and optimization. Medical Information Science Reference, 2010.

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Kambayashi, Yasushi. Multi-agent applications with evolutionary computation and biologically inspired technologies: Intelligent techniques for ubiquity and optimization. Medical Information Science Reference, 2010.

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

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Mirjalili, Seyedali, and Jin Song Dong. Multi-Objective Optimization using Artificial Intelligence Techniques. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-24835-2.

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Olsson, Andrea E. Particle swarm optimization: Theory, techniques, and applications. Nova Science Publishers, 2010.

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Book chapters on the topic "Intelligent optimization techniques"

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Guha, Dipayan, Provas Kumar Roy, Subrata Banerjee, and Shubhi Purwar. "Optimization Techniques." In Application of Intelligent Control Algorithms to Study the Dynamics of Hybrid Power System. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0444-8_3.

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Kaushik, Deepika, and Mohammad Nadeem. "Portfolio optimization using batch enabled particle swarm optimization." In Intelligent Computing and Communication Techniques. CRC Press, 2025. https://doi.org/10.1201/9781003635680-15.

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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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Sakshi, Sakshi, and Ankit Sharma. "Relational Database Performance Optimization Techniques." In Advances in Intelligent Systems Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-716-8_10.

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Ghatee, Mehdi. "Optimization Techniques in Intelligent Transportation Systems." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56689-0_4.

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Cam, Nguyen Tan, Trinh Gia Huy, Vo Ngoc Tan, Phuc Nguyen, and Sang Vo. "Android Application Behavior Monitor by Using Hooking Techniques." In Intelligent Computing and Optimization. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-50327-6_34.

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Rashida, Maliha, Fariha Iffath, Rezaul Karim, and Mohammad Shamsul Arefin. "Trends and Techniques of Biomedical Text Mining: A Review." In Intelligent Computing & Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93247-3_92.

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Ghosh, Promila, M. Raihan, Md Mehedi Hassan, Laboni Akter, Sadika Zaman, and Md Abdul Awal. "Fake News Detection of COVID-19 Using Machine Learning Techniques." In Intelligent Computing & Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93247-3_46.

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Hemadri, Raghu Vamshi, and Ravi Kumar Jatoth. "Jaya Algorithm Based Intelligent Color Reduction." In Advanced Engineering Optimization Through Intelligent Techniques. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8196-6_18.

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Shinde, V. B., and P. J. Pawar. "Trajectory Optimization of an Industrial Robot Using Teaching–Learning-Based Optimization." In Advanced Engineering Optimization Through Intelligent Techniques. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9285-8_63.

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Conference papers on the topic "Intelligent optimization techniques"

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Dheshmuk, Mallikarjun, and S. B. Kumbalavati. "Optimization Techniques for Reconfigurable Intelligent Surfaces in 6G." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10690066.

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Zhu, Xiaohui, Chunhua Hu, and Ling Luo. "Research on Intelligent Portfolio Optimization Model Based on Big Data and Artificial Intelligence." In 2024 International Conference on Interactive Intelligent Systems and Techniques (IIST). IEEE, 2024. http://dx.doi.org/10.1109/iist62526.2024.00101.

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Choudhury, Priyabati, Zahid Ahmed, and Sunitha B. K. "Analyzing Intelligent Optimization Techniques for 6G Radio Resource Allocation." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725020.

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Zhou, Jiehong, and Jie Yu. "Construction and Optimization of An Intelligent Student Rating System." In 2024 International Conference on Interactive Intelligent Systems and Techniques (IIST). IEEE, 2024. http://dx.doi.org/10.1109/iist62526.2024.00144.

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Narwadkar, Yogita P., and Anant M. Bagade. "Advanced Optimization Techniques for Effective Text Summarization." In 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA). IEEE, 2024. https://doi.org/10.1109/icisaa62385.2024.10829069.

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Najeeb, Phlip Y., Ashraf Aboshosha, and Ayman Haggag. "Intelligent Optimization of Photovoltaic Stations Performance Relying on Integrated Solar Techniques." In 2024 25th International Middle East Power System Conference (MEPCON). IEEE, 2024. https://doi.org/10.1109/mepcon63025.2024.10850229.

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Mishra, Gaurav, Khushi Kumari, and Chethan Sharma. "Optimization Techniques in Electronics: Advances, Challenges, and Future Directions." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10985323.

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Zheng, Yijian. "Strategies for Graph Optimization in Deep Learning Compilers." In 2024 International Conference on Interactive Intelligent Systems and Techniques (IIST). IEEE, 2024. http://dx.doi.org/10.1109/iist62526.2024.00086.

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M, Mallegowda, Saanvi Nair, Sahil Khirwal, Manik Animesh, Vasuman Mishra, and Anita Kanavalli. "Analysis of optimization Techniques for Dynamic Neural Networks." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915299.

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Liu, Ziyu, and Xue Wang. "Research on User Experience Optimization of Intelligent Virtual Assistant Based on Deep Learning." In 2024 International Conference on Interactive Intelligent Systems and Techniques (IIST). IEEE, 2024. http://dx.doi.org/10.1109/iist62526.2024.00043.

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Reports on the topic "Intelligent optimization techniques"

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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 trajectory optimization, model predictive control (MPC), Lyapunov stability, and hierarchical RL for ensuring safe and robust control in complex environments. Through case studies in self-driving vehicles, autonomous drones, robotic manipulation, healthcare robotics, and multi-agent systems, this research highlights the trade-offs between model-based and model-free approaches, as well as the challenges of scalability, sample efficiency, hardware acceleration, and ethical AI deployment. The findings underscore the importance of hybrid RL-control frameworks, real-world RL training, and policy optimization techniques in advancing robotic intelligence and autonomous decision-making. Keywords: Optimal control, reinforcement learning, model-based RL, model-free RL, dynamic programming, policy optimization, Hamilton-Jacobi-Bellman equations, actor-critic methods, deep reinforcement learning, trajectory optimization, model predictive control, Lyapunov stability, hierarchical RL, multi-agent RL, robotics, self-driving cars, autonomous drones, robotic manipulation, AI-driven automation, safety in RL, hardware acceleration, sample efficiency, hybrid RL-control frameworks, scalable AI.
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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 knowledge, creativity, and innovation. It requires new approaches and methods for solving complex problems, discovering new opportunities, and creating value in various domains of science, business,and society. Computational intelligence and data science are two key disciplines that can provide such approaches and methods by exploiting the power of data, algorithms, models, and systems to enable intelligent decision making, learning, adaptation, optimization, and discovery. The papers in this proceedings cover a wide range of topics related to computational intelligence and data science for the innovation economy. They include theoretical foundations, novel techniques, and innovative applications. The papers were selected and revised based on the feedback from the program committe members and reviewers who ensured their high quality. We would like to thank all the authors who submitted their papers to M3E2 2022. We also appreciate the keynote speakers who shared their insights and visions on the current trends and future directions of computational intelligence and data science for the innovation economy. We acknowledge the support of our sponsors, partners, and organizers who made this conference possible despite the challenging circumstances caused by the ongoing war in Ukraine. Finally, we thank all the participants who attended the conference online and contributed to its success.
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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayesian deep learning. These techniques enhance AI's ability to handle uncertain, noisy, and high-dimensional data while ensuring scalability, interpretability, and trustworthiness in applications such as robotics, financial modeling, autonomous systems, and healthcare AI. Case studies demonstrate how stochastic computation improves self-driving car navigation, financial risk assessment, personalized medicine, and reinforcement learning-based automation. The findings underscore the importance of integrating probabilistic modeling with deep learning, reinforcement learning, and optimization techniques to develop AI systems that are more adaptable, scalable, and uncertainty-aware. Keywords Stochastic computation, Bayesian inference, probabilistic AI, Monte Carlo methods, Markov Chain Monte Carlo (MCMC), variational inference, uncertainty quantification, stochastic optimization, Bayesian deep learning, reinforcement learning, probabilistic graphical models, stochastic gradient descent (SGD), uncertainty-aware AI, probabilistic reasoning, risk assessment, AI in robotics, AI in finance, AI in healthcare, decision-making under uncertainty, trustworthiness in AI, scalable AI, interpretable AI.
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Pasupuleti, Murali Krishna. Augmented Human Intelligence: Converging Generative AI, Quantum Computing, and XR for Enhanced Human-Machine Synergy. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv525.

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Abstract: Augmented Human Intelligence (AHI) represents a paradigm shift in human-AI collaboration, leveraging Generative AI, Quantum Computing, and Extended Reality (XR) to enhance cognitive capabilities, decision-making, and immersive interactions. Generative AI enables real-time knowledge augmentation, automated creativity, and adaptive learning, while Quantum Computing accelerates AI optimization, pattern recognition, and complex problem-solving. XR technologies provide intuitive, immersive environments for AI-driven collaboration, bridging the gap between digital and physical experiences. The convergence of these technologies fosters hybrid intelligence, where AI amplifies human potential rather than replacing it. This research explores AI-augmented cognition, quantum-enhanced simulations, and AI-driven spatial computing, addressing ethical, security, and societal implications of human-machine synergy. By integrating decentralized AI governance, privacy-preserving AI techniques, and brain-computer interfaces, this study outlines a scalable framework for next-generation augmented intelligence applications in healthcare, enterprise intelligence, scientific discovery, and immersive learning. The future of AHI lies in hybrid intelligence systems that co-evolve with human cognition, ensuring responsible and transparent AI augmentation to unlock new frontiers in human potential. Keywords: Augmented Human Intelligence, Generative AI, Quantum Computing, Extended Reality, XR, AI-driven Cognition, Hybrid Intelligence, Brain-Computer Interfaces, AI Ethics, AI-enhanced Learning, Spatial Computing, Quantum AI, Immersive AI, Human-AI Collaboration, Ethical AI Frameworks.
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Pasupuleti, Murali Krishna. Neuromorphic Nanotech: 2D Materials for Energy-Efficient Edge Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rr325.

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Abstract The demand for energy-efficient, real-time computing is driving the evolution of neuromorphic computing and edge AI systems. Traditional silicon-based processors struggle with power inefficiencies, memory bottlenecks, and scalability limitations, making them unsuitable for next-generation low-power AI applications. This research report explores how 2D materials, such as graphene, transition metal dichalcogenides (TMDs), black phosphorus, and MXenes, are enabling the development of neuromorphic architectures that mimic biological neural networks for high-speed, ultra-low-power computation. The study examines synaptic transistors, memristors, and AI-driven optimization techniques that enhance the performance of neuromorphic chips for autonomous AI, smart IoT systems, and real-time decision-making at the edge. Additionally, it discusses manufacturing challenges, economic feasibility, and policy implications related to large-scale adoption of 2D materials in nanoelectronics and semiconductor industries. Through case studies and emerging trends, this report provides a roadmap for integrating neuromorphic nanotech into mainstream AI-powered edge computing, ensuring scalability, sustainability, and high-performance intelligence for next-generation computing applications. Keywords: Neuromorphic computing, 2D materials, energy-efficient AI, edge computing, graphene, transition metal dichalcogenides, black phosphorus, MXenes, synaptic transistors, memristors, nanotechnology, low-power AI, spiking neural networks, AI-driven material discovery, quantum simulations, AI hardware optimization, semiconductor nanotech, real-time AI inference, autonomous AI, AI-powered IoT, sustainable computing.
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