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Journal articles on the topic 'Intelligent optimization techniques'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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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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Surianarayanan, Chellammal, John Jeyasekaran Lawrence, Pethuru Raj Chelliah, Edmond Prakash, and Chaminda Hewage. "A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)." Sensors 23, no. 3 (2023): 1279. http://dx.doi.org/10.3390/s23031279.

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Artificial Intelligence (Al) models are being produced and used to solve a variety of current and future business and technical problems. Therefore, AI model engineering processes, platforms, and products are acquiring special significance across industry verticals. For achieving deeper automation, the number of data features being used while generating highly promising and productive AI models is numerous, and hence the resulting AI models are bulky. Such heavyweight models consume a lot of computation, storage, networking, and energy resources. On the other side, increasingly, AI models are being deployed in IoT devices to ensure real-time knowledge discovery and dissemination. Real-time insights are of paramount importance in producing and releasing real-time and intelligent services and applications. Thus, edge intelligence through ondevice data processing has laid down a stimulating foundation for real-time intelligent enterprises and environments. With these emerging requirements, the focus turned towards unearthing competent and cognitive techniques for maximally compressing huge AI models without sacrificing AI model performance. Therefore, AI researchers have come up with a number of powerful optimization techniques and tools to optimize AI models. This paper is to dig deep and describe all kinds of model optimization at different levels and layers. Having learned the optimization methods, this work has highlighted the importance of having an enabling AI model optimization framework.

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VASYLKIVSKYI, Mykola, Ganna VARGATYUK, and Olha BOLDYREVA. "INTELLIGENT OPTIMIZATION OF MULTIPLE ACCESS INFOCOMMUNICATION NETWORKS." Herald of Khmelnytskyi National University. Technical sciences 315, no. 6 (2022): 32–39. http://dx.doi.org/10.31891/2307-5732-2022-315-6(2)-32-39.

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The methods of multiple access with multiplexing of resources are studied and the advantages and disadvantages of orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are considered. A comparative analysis of data transmission schemes in the radio network was also performed, taking into account resource planning, in particular: transmission with service information and transmission without service information. The structure of the NOMA uplink receiver based on OFDM signals is proposed. The peculiarities of providing supermassive connection within limited radio resources on the basis of grantless access using NOMA have been studied. At the same time, methods of solving the problems inherent in the current application of GF-transmission and NOMA in the implementation of supermassive connection to the access network based on 6G technology were considered. Prospects for the introduction of an artificial intelligence transmitter based on a multiple access transmission scheme with low cost, low PAPR, low delay, high reliability and wide connectivity are determined. And features of artificial intelligence receiver design using artificial intelligence / machine learning techniques that can play a role in facilitating MUD design for NOMA are also considered.

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Venkata Siva Prasad Bharathula. "LLM Serving Optimization Techniques: A Comprehensive Analysis." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 174–81. https://doi.org/10.32996/jcsts.2025.7.5.23.

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This article presents a comprehensive analysis of optimization techniques for serving Large Language Models (LLMs), addressing the critical challenges posed by their exponential growth in size and computational requirements. This paper examines four key areas of optimization: hardware acceleration, serving architecture design, model compression, and dynamic scaling strategies. The article synthesizes findings from multiple studies demonstrating significant improvements in memory efficiency, throughput, latency, and cost-effectiveness through innovative approaches, including parameter-centric memory management, near-storage processing, adaptive batching, model parallelism, quantization, pruning, and intelligent caching. Also explore promising future directions in hardware-software co-design and advanced compiler optimizations that could further democratize access to these powerful models. The collective impact of these techniques enables more efficient deployment of LLMs across diverse computing environments, from high-performance data centers to resource-constrained edge devices.

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C. Dalwai, Kalpana. "AN OVERVIEW OF SWARM INTELLIGENCE IN ARTIFICIAL INTELLIGENT SYSTEMS." International Journal of Advanced Research 9, no. 08 (2021): 673–75. http://dx.doi.org/10.21474/ijar01/13314.

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Swarm intelligence refers to a kind of problem-solving ability that emerges in the interactions of simple information-processing units. The concept of a swarm suggests multiplicity, stochasticity, randomness, and messiness. Advancement of technology has led to problems that are complex and more challenging.Swarm intelligence techniques were mostly developed for solving optimization problems.

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NDIDI, OSEGI EMMANUEL, WOKOMA BIOBELE ALEXANDER, OJUKA OTONYE, BRUCE-ALLISON SA, and CHUJOR CORNELIUS CHICHI. "SWAMI: A SWARM-INTELLIGENT OPTIMIZATION TECHNIQUE FOR VOLTAGE COLLAPSE MITIGATION." Journal of Engineering Studies and Research 28, no. 2 (2022): 33–47. http://dx.doi.org/10.29081/jesr.v28i2.004.

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In this paper, a voltage collapse optimization system based on comparative studies of swarm-intelligent techniques is proposed for voltage collapse mitigation in power system network. The approach draws inspiration from the idea of utilizing the intelligent behavior of swarm-based artificial machine intelligence technique coined SWAMI for voltage collapse minimization or prevention through dynamic shunt compensation of overloaded power network buses. Several simulation studies have been conducted considering three very popular and successful SWAMI agents – the PSOM, BCOM and ACOM on an IEEE benchmark power network with promising results. Simulation studies showed that the PSOM SWAMI exhibited the most stable response in terms of voltage profile collapse and recovery from voltage collapse state after voltage sensitivity studies. Safe margins of loading and optimal shunt compensations are determined based on the SWAMI techniques.

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N., Yuvaraj. "Improved Intelligent Techniques of Ensemble Data Clustering Method Using Bees Swarm Optimization Ensemble Approach." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 1762–73. http://dx.doi.org/10.37200/ijpr/v24i5/pr201847.

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Siddhartha, Peddi, Akash Surendran, and Siddhant Saxena. "An Intelligent Approach to Energy Harvesting Optimization in Electric Vehicles through Data Science Techniques." International Journal of Research Publication and Reviews 4, no. 12 (2023): 2999–3009. http://dx.doi.org/10.55248/gengpi.4.1223.123312.

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Pote, Yash, Anuj Godanme, Vishal Pilare, Omkar Shinde, and Jitendra Sawant. "Optimization of PMSM Motor Control Systems for High-Performance Electric Vehicles Using Intelligent Techniques." International Journal of Research Publication and Reviews 6, no. 6 (2025): 869–75. https://doi.org/10.55248/gengpi.6.0625.2021.

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Osadciw, Lisa, Kalyan Veeramachaneni, Weihua Gao, and Ganapathi Kamath. "Intelligent optimization techniques making practical emergency responder sensor networks." OPSEARCH 46, no. 2 (2009): 246–58. http://dx.doi.org/10.1007/s12597-009-0016-z.

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Elkazaz, Mahmoud H., Ayman A. Hoballah, and Ahmed M. Azmy. "Operation optimization of distributed generation using artificial intelligent techniques." Ain Shams Engineering Journal 7, no. 2 (2016): 855–66. http://dx.doi.org/10.1016/j.asej.2016.01.008.

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Fattahi, Hadi, Hossein Ghaedi, and Danial Jahed Armaghani. "Improving Shallow Foundation Settlement Prediction through Intelligent Optimization Techniques." Computer Modeling in Engineering & Sciences 143, no. 1 (2025): 747–66. https://doi.org/10.32604/cmes.2025.062390.

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Khasawneh, Mohammed A., and Anjali Awasthi. "Intelligent Meta-Heuristic-Based Optimization of Traffic Light Timing Using Artificial Intelligence Techniques." Electronics 12, no. 24 (2023): 4968. http://dx.doi.org/10.3390/electronics12244968.

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This research examines worldwide concerns over traffic congestion, encompassing aspects such as security, parking, pollution, and congestion. It specifically emphasizes the importance of implementing appropriate traffic light timing as a means to mitigate these issues. The research utilized a dataset from Montreal and partitioned the simulated area into various zones in order to determine congestion levels for each individual zone. A range of prediction algorithms has been employed, such as Long Short-Term Memory (LSTM), Decision Tree (DT), Recurrent Neural Network (RNN), Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressive Integrated Moving Average (SARIMA), to predict congestion levels at each traffic light. This information was used in a mathematical formulation to minimize the average waiting time for vehicles inside the road network. Many meta-heuristics were analyzed and compared, with the introduction of an Enhanced Bat Algorithm (EBAT) suggested for addressing the traffic signal optimization problem. Three distinct scenarios are described: fixed (with a constant green timing of 40 s), dynamic (where the timing changes in real-time based on the current level of congestion), and adaptive (which involves predicting congestion ahead of time). The scenarios are studied with low and high congestion scenarios in the road network. The Enhanced Bat Algorithm (EBAT) is introduced as a solution to optimize traffic signal timing. It enhances the original Bat algorithm by incorporating adaptive parameter tuning and guided exploration techniques that are informed by predicted congestion levels. The EBAT algorithm provides a more effective treatment for congestion problems by decreasing travel time, enhancing vehicle throughput, and minimizing pollutant emissions.

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Faisal, Mohammed, Hassan Mathkour, and Mansour Alsulaiman. "AntStar: Enhancing Optimization Problems by Integrating an Ant System andA⁎Algorithm." Scientific Programming 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/5136327.

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Recently, nature-inspired techniques have become valuable to many intelligent systems in different fields of technology and science. Among these techniques, Ant Systems (AS) have become a valuable technique for intelligent systems in different fields. AS is a computational system inspired by the foraging behavior of ants and intended to solve practical optimization problems. In this paper, we introduce the AntStar algorithm, which is swarm intelligence based. AntStar enhances the optimization and performance of an AS by integrating the AS andA⁎algorithm. Applying the AntStar algorithm to the single-source shortest-path problem has been done to ensure the efficiency of the proposed AntStar algorithm. The experimental result of the proposed algorithm illustrated the robustness and accuracy of the AntStar algorithm.

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NAVEEN REDDY THATIGUTLA. "AI-driven innovations in network and storage optimization: Transforming infrastructure management." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2984–91. https://doi.org/10.30574/wjaets.2025.15.2.0885.

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Artificial Intelligence is revolutionizing network and storage infrastructure management by enabling intelligent optimization across increasingly complex and distributed environments. This article explores the theoretical foundations and practical applications of AI-driven approaches to infrastructure optimization, examining how machine learning techniques transform traditional management paradigms. The evolution from rule-based systems to sophisticated learning algorithms has enabled dynamic traffic management, predictive maintenance, intelligent resource allocation, and automated performance optimization. Despite demonstrating significant benefits, the integration of AI into infrastructure environments presents substantial challenges related to data quality, security considerations, organizational factors, and standardization requirements. These challenges necessitate innovative solutions that bridge technical and operational domains while ensuring appropriate governance of increasingly autonomous systems. Future directions in this field include edge computing integration, explainable AI development, cross-domain optimization approaches, and enhanced human-AI collaboration frameworks that will shape the next generation of intelligent infrastructure management systems.

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Itoro, Afolayan Blessing, Arka Ghosh, Calderın Jenny Fajardo, and Antonio D. Masegosa. "Emerging Trends in Machine Learning assisted Optimization Techniques Across Intelligent Transportation systems." IEEE Access VOLUME 12, 2024 (November 6, 2024): 173981–4005. https://doi.org/10.5281/zenodo.14044639.

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Artificial intelligence (AI) plays a critical role in Intelligent Transport Systems (ITS) as urban areas grow by processing data for safety enhancements, predictive analysis, and traffic management. This results in better traffic control, lower emissions, and preventative actions to lessen the effects of accidents. Despite these developments, there isn’t a thorough academic analysis that covers a variety of optimization strategies for transportation AI models. By presenting an in-depth analysis of AI optimization methodsand their uses in ITSs, this work seeks to close this knowledge gap and give academics important new information on possible directions for future research. Model-based optimization approaches, reinforcement learning techniques, model-predictive control techniques, and generative AI techniques are the four areas into which this study divides AI optimization techniques for the sake of structure, clarity, and comparative analysis. Subcategories of optimization techniques and their corresponding applications are explored, and each category is thoroughly addressed. Researchers will be better able to comprehend the state of AI optimization for transportation management today and in the future thanks to this methodical methodology. The most cutting-edge optimization methods created in the last five years are summarized in this review. This work acts as a compass for future research initiatives targeted at developing scalable and adaptable AI solutions for transportation management by identifying common approaches and highlighting research needs.

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Tian, Guangdong, Zhiwu Li, Dexin Yu, Amir M. Fathollahi-Fard, Lisheng Jin, and Xingyu Jiang. "Editorial Conclusion for the Special Issue “Advanced Transportation Technologies and Symmetries in Intelligent Transportation Systems”." Symmetry 14, no. 7 (2022): 1439. http://dx.doi.org/10.3390/sym14071439.

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The recent advances in the intelligent transportation systems (ITS) are reviewed in this Special Issue in which many techniques in mathematics, artificial intelligence, machine learning, automatic control, and optimization theory were considered to address the ITS based on recent technologies, methods and symmetries [...]

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Isaac, Annie Shalom, and Cornelius Neumann. "Optimization of freeform surfaces using intelligent deformation techniques for LED applications." Advanced Optical Technologies 7, no. 1-2 (2018): 67–80. http://dx.doi.org/10.1515/aot-2017-0074.

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AbstractFor many years, optical designers have great interests in designing efficient optimization algorithms to bring significant improvement to their initial design. However, the optimization is limited due to a large number of parameters present in the Non-uniform Rationaly b-Spline Surfaces. This limitation was overcome by an indirect technique known as optimization using freeform deformation (FFD). In this approach, the optical surface is placed inside a cubical grid. The vertices of this grid are modified, which deforms the underlying optical surface during the optimization. One of the challenges in this technique is the selection of appropriate vertices of the cubical grid. This is because these vertices share no relationship with the optical performance. When irrelevant vertices are selected, the computational complexity increases. Moreover, the surfaces created by them are not always feasible to manufacture, which is the same problem faced in any optimization technique while creating freeform surfaces. Therefore, this research addresses these two important issues and provides feasible design techniques to solve them. Finally, the proposed techniques are validated using two different illumination examples: street lighting lens and stop lamp for automobiles.

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Cheng, Tao. "Intelligent Grid Optimization and Fault Prediction Based on Machine Learning." International Education Forum 2, no. 6 (2024): 23–29. http://dx.doi.org/10.26689/ief.v2i6.7941.

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With the intelligent development of power systems, the demand for intelligent grids in energy management, fault detection, and prediction is continuously increasing. Traditional optimization techniques and fault prediction methods are inadequate for the efficient operation of modern power grids due to their limitations. This paper explores intelligent grid optimization and fault prediction methods based on machine learning. By analyzing the shortcomings of current intelligent grid optimization technologies and fault prediction methods, it elucidates the application advantages of machine learning in grid optimization and fault prediction and provides a detailed introduction to relevant algorithms and their implementation processes. The research results show that machine learning technology has significant advantages in improving grid optimization efficiency and fault prediction accuracy, providing new solutions for the stable operation of intelligent grids.

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Long, Teng, Zhangbing Zhou, Gerhard Hancke, Yang Bai, and Qi Gao. "A Review of Artificial Intelligence Technologies in Mineral Identification: Classification and Visualization." Journal of Sensor and Actuator Networks 11, no. 3 (2022): 50. http://dx.doi.org/10.3390/jsan11030050.

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Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural language processing, and expert systems. In recent years, the availability of large datasets, the development of effective algorithms, and access to powerful computers have led to unprecedented success in artificial intelligence. This powerful tool has been used in numerous scientific and engineering fields including mineral identification. This paper summarizes the methods and techniques of artificial intelligence applied to intelligent mineral identification based on research, classifying the methods and techniques as artificial neural networks, machine learning, and deep learning. On this basis, visualization analysis is conducted for mineral identification of artificial intelligence from field development paths, research hot spots, and keywords detection, respectively. In the end, based on trend analysis and keyword analysis, we propose possible future research directions for intelligent mineral identification.

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Sadegheih, A. "Optimization of network planning by the novel hybrid algorithms of intelligent optimization techniques." Energy 34, no. 10 (2009): 1539–51. http://dx.doi.org/10.1016/j.energy.2009.06.047.

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Wei, Wei, Li Jie Ding, and Yan Jiao Liu. "A Review of Regional Reactive Power Optimization Techniques." Advanced Materials Research 986-987 (July 2014): 1360–64. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1360.

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With increasingly complex of the power grid structure and increasingly user requirements of power quality, Power grid voltage reactive power optimization is still the difficult points in power system operation control. This paper introduces the general optimization methods of multi-objective reactive power optimization, intelligent algorithm, the development of the hybrid method, and their respective advantages, disadvantages and improvement; It also analyzes and summarizes the simplification of search space for reactive power optimization and the key issues and research development trend of the real-time reactive voltage control system.

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Zhu, Rongsheng, Zhixin Zhang, Yangyang Cao, et al. "CPDOS: A Web-Based AI Platform to Optimize Crop Planting Density." Agronomy 13, no. 10 (2023): 2465. http://dx.doi.org/10.3390/agronomy13102465.

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Increasing crop yield is a significant objective in modern agriculture, with adjusted planting density and rational fertilization strategies standing out as the foremost approaches for attaining such a goal. Through the use of modern artificial intelligence techniques such as genetic algorithms and neural networks, the CPDOS (Crop Planting Density Optimization System), an online intelligent system that can automate the modeling, optimization, and analysis of the two models, was developed in the present study. The goal of the system is to optimize the planting density model and fertilizer application in combination with other computer system development techniques. The CPDOS comprises three main modules: yield density optimization module, optimal planting density range module, and fertilization and planting density optimization module. The three modules are complemented by two modules for data input and result visualization, culminating in the comprehensive process of optimizing planting density and fertilizer allocation through the CPDOS. The CPDOS was tested using potato, corn, and soybean data, and the results show that the optimization effects of planting density and fertilizer application were satisfactory. The CPDOS is an automated crop planting optimization system that integrates algorithms and models and is driven by artificial intelligence technology. The introduction of the CPDOS reduces the barriers to utilizing these algorithms and models, facilitating wider adoption of intelligently optimized planting technology. The platform’s launch will accelerate the swift advancement of this field.

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TOMATSU, Yuat, Tomoyuki HIROYASU, Masato YOSHIMI, and Mitsunori MIKI. "303 gPot : Intelligent Compiler for GPGPU using Combinatorial Optimization Techniques." Proceedings of OPTIS 2010.9 (2010): _303–1_—_303–4_. http://dx.doi.org/10.1299/jsmeoptis.2010.9._303-1_.

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Gupta, Dolly. "Intelligent techniques for optimization of a two tank system controller." CSI Transactions on ICT 4, no. 2-4 (2016): 79–82. http://dx.doi.org/10.1007/s40012-016-0102-3.

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Lambiase, Francesco. "Optimization of shape rolling sequences by integrated artificial intelligent techniques." International Journal of Advanced Manufacturing Technology 68, no. 1-4 (2013): 443–52. http://dx.doi.org/10.1007/s00170-013-4742-2.

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Benlahbib, Boualam, Farid Bouchafaa, Saad Mekhilef, and Noureddine Bouarroudj. "Wind Farm Management using Artificial Intelligent Techniques." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 3 (2017): 1133. http://dx.doi.org/10.11591/ijece.v7i3.pp1133-1144.

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This paper presents a comparative study between genetic algorithm and particle swarm optimization methods to determine the optimal proportional–integral (PI) controller parameters for a wind farm management algorithm. This study primarily aims to develop a rapid and stable system by tuning the PI controller, thus providing excellent monitoring for a wind farm system. The wind farm management system supervises the active and reactive power of the wind farm by sending references to each wind generator. This management system ensures that all wind generators achieve their required references. Furthermore, the entire management is included in the normal controlling power set points of the wind farm as designed by a central control system. The performance management of this study is tested through MATLAB/Simulink simulation results for the wind farm based on three doublyfed induction generators

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Cai, Wenchao, Chenglong Li, Kodjo Agbossou, Pierre Bénard, and Jinsheng Xiao. "A review of hydrogen-based hybrid renewable energy systems: Simulation and optimization with artificial intelligence." Journal of Physics: Conference Series 2208, no. 1 (2022): 012012. http://dx.doi.org/10.1088/1742-6596/2208/1/012012.

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Abstract With the massive use of traditional fossil fuels, greenhouse gas emissions are increasing, and environmental pollution is becoming an increasingly serious problem, which led to an imminent energy transition. Therefore, the development and application of renewable energy are particularly important. This paper reviews a wide range of issues associated with hybrid renewable energy systems (HRESs). The issues concerning system configurations, energy storage options, simulation and optimization with artificial intelligence are discussed in detail. Storage technology options are introduced for stand-alone (off-grid) and grid-connected (on-grid) HRESs. Different optimization methodologies, including classical techniques, intelligent techniques, hybrid techniques and software tools for sizing system components, are presented. Besides, the artificial intelligence methods for optimizing the solar/wind HRESs are discussed in detail.

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Dahiru, Ahmed Tijjani, Tan Chee Wei, Habibu Guda Rano, Mustapha Sani Sumaila, Adamu Abdullahi Maidallah, and Chukwunwike Emmanuel Attamah. "A Comparative Analysis of Optimization Techniques Used in the Sizing of a Residential Nanogrid." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 127, no. 2 (2025): 106–18. https://doi.org/10.37934/arfmts.127.2.106118.

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Economic use of resources and technologies is mainly achieved using optimization frameworks. Optimizations for renewable energy system planning and operations are mainly implemented using either or combination of classical, intelligent-based, or off-the-shelf software techniques (tools). Each of the homogenous technique implementations exhibits a form or more of its opportunities and limitations. Classical techniques are opaque and loaded with computational burdens, however, they tend to be fast in achieving global solutions without branching at local solutions. The intelligent-based techniques such as metaheuristics are simple and transparent, however, the results they produce are not always optimal, even though acceptable. The software tools are inflexible for customized applications, but the results they produce are always optimal and fast implementations. This paper considers the constraints of Nigerian semiarid weather data, renewable energy components and residential demands to configure a nanogrid using Nested Integer Linear Programming, Particle Swarm Optimization and Hybrid Optimization of Multiple Energy Resources optimization software as samples of the foregoing classification of optimization tools. The objective is to compare the performance of each of the tools’ homogeneity, to derive their benefits and drawbacks. Results show that Hybrid Optimization of Multiple Energy Resources has the highest configurational capacity of 46.5 kW with the lowest Levelized Cost of Energy of 0.2220 $/kWh. The Nested Integer Linear Programming having the lowest capacity of 21.26 kW derived 0.3375 $/kWh as Levelized Cost of Energy, while Particle Swarm Optimization with an average capacity of 27.05 kW obtained the Levelized Cost of Energy of 0.4777 $/kWh. This implies that higher investment derives lower energy costs. Hence, future use of the techniques’ homogeneity or otherwise may be subject to achieving balances between the benefits and drawbacks exhibited by the techniques and underlying renewable energy-based objectives.

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Jaiswal, Sakshi, Awdhesh Gupta, and Shivam Kumar Kanojiya. "Optimization of Energy Consumption via Artificial Intelligence: A Study." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 8, no. 01 (2016): 26–33. http://dx.doi.org/10.18090/samriddhi.v8i1.11409.

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In the future intelligent machines will replace or enhance human capabilities in many areas. Artificial Intelligence is the intelligence exhibited by machines or software.”John McCarthy” who coined the tem in 1955 defines it as the “science and engineering of making intelligent machines”. As we are aware of the fact that energy consumption has grown tremendously over a few decades in all over the world which is environmentally unfriendly. It is essential at this stage of development to pause and critically examine the state of affairs via the application of AI in energy conservation and environmental system engineering. In this paper we describe in a general way on how the existing applications of AI techniques provide intelligent solution to optimize the energy conservation now and in the future. Also, how Wireless Sensor and Actuator Networks are used to remotely monitor and control the environment according to the decisions made by the centralized reasoner and EEMS(Energy Efficiency Management System)provides effective energy saving measures and high quality energy conservation services Energy efficiency has nowadays become one of the most challenging task for both academic and commercial organizations and this has boosted research on novel fields.

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Siddhant, Garje* &. Dr. Anita Kumari**. "APPLICATION OF ARTIFICIAL INTELLIGENCE IN PETROLEUM INDUSTRY." International Journal of Engineering Research and Modern Education (IJERME) 8, no. 1 (2023): 13–18. https://doi.org/10.5281/zenodo.7663138.

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Artificial intelligence (AI) has been widely applied to optimization challenges in the petroleum exploration and production industry in recent years. With the new industry interest and enthusiasm for smart wells, intelligent fields, and real-time analysis and interpretation of enormous amounts of data for process optimization, our industry's need for powerful, resilient, and intelligent technologies has expanded dramatically. This survey provides a thorough literature analysis based on various types of AI algorithms, their application areas in the petroleum sector, and the geographical regions where they are being developed. To that end, AI methods are divided into four categories: evolutionary algorithms, swarm intelligence, fuzzy logic, and artificial neural networks. Furthermore, these types of algorithms in terms of their applications in petroleum engineering are investigated. Furthermore, the hybridization and/or combination of multiple AI techniques can be successfully used to address critical optimization problems and achieve superior results.

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Asif, Muhammad, Hang Shen, Chunlin Zhou, et al. "Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions." Sustainability 15, no. 10 (2023): 8298. http://dx.doi.org/10.3390/su15108298.

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Intelligent manufacturing is considered among the most important elements of the modern industrial revolution, which includes digitalization, networking, and the development of the intelligent manufacturing industry. With the progressive development of modern information technology, particularly the new generation of artificial intelligence (AI) technology, many new opportunities are coming into existence for intelligent machine tool (IMT) development. Intelligent machine tools offer diverse advantages, including learning and optimizing machining processes, error compensation, energy savings, and failure prevention. The paper focuses on the machine tool market in terms of global production, the leading machine tool-producing countries, and the leading countries’ market share in machine tool production. Moreover, the usage of various artificial intelligence techniques in intelligent machining operations is also considered in this comprehensive review, including machining parameter optimization, tool condition monitoring (TCM), and chatter vibration management of intelligent machine tools. Furthermore, future challenges for the machine tool industry are also highlighted.

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Brökelmann, M., A. Unger, T. Meyer, et al. "Kupferbondverbindungen intelligent herstellen/Intelligent production of heavy copper wire bonds." wt Werkstattstechnik online 106, no. 07-08 (2016): 512–19. http://dx.doi.org/10.37544/1436-4980-2016-07-08-46.

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Ziel dieses Innovationsprojekts des Spitzenclusters „it’s OWL – Intelligente Technische Systeme OstWestfalen-Lippe“ ist die Entwicklung von selbstoptimierenden Verfahren, um unter variablen Produktionsbedingungen zuverlässige Kupferbondverbindungen herstellen zu können. Die Ultraschall-Drahtbondmaschine erhält die Fähigkeit, sich automatisch an veränderte Bedingungen anzupassen. Hierzu wird der gesamte Prozess der Ultraschall-Verbindungsbildung modelliert und neueste Verfahren der Selbstoptimierung angewandt. Die Evaluierung erfolgt anhand eines Prototypen in Form einer modifizierten Bondmaschine. &nbsp; It is the aim of this innovation-project to develop a self-optimization system for ultrasonic copper wire bonding. It is part of the leading edge cluster “it’s OWL”. The bonding machine will be able to react autonomously to changing boundary conditions to ensure constant and reliable bonding results. For this, the hole bonding process is modeled in great detail and newest self-optimization techniques are utilized. A prototype-system incorporated in a serial machine is used for evaluation.

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Anantwar, Harsha, and Shanmukh Sunda. "Intelligent Optimized Voltage Control for Hybrid Off-Grid Power Systems." Journal of Asian Energy Studies 7 (February 8, 2023): 39–47. http://dx.doi.org/10.24112/jaes.070003.

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Hybrid off-grid power systems with different renewable and non-renewable energy sources, such as wind, photovoltaics, and diesel generation, have a wide application scope in regions where grid extension is not possible. To supply quality power, hybrid off-grid power systems need proper reactive power management to deal with randomly changing load and supply. In particular, to realize dependable and quality power supply, hybrid off-grid power systems require suitable and efficient control techniques. A properly tuned controller of reactive power sources is crucial to maintain a prescribed voltage profile. Computational intelligence techniques such as particle swarm optimization can provide desired and acceptable solutions for optimization problems. In this study, we applied computational intelligent techniques for optimal control of reactive power sources, such as photovoltaic inverters and automatic voltage regulators for synchronous generators in diesel engines, to investigate dynamic voltage profile stability through reactive power management.

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Yang, Liwei, Ping Li, Song Qian, et al. "Path Planning Technique for Mobile Robots: A Review." Machines 11, no. 10 (2023): 980. http://dx.doi.org/10.3390/machines11100980.

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Mobile robot path planning involves designing optimal routes from starting points to destinations within specific environmental conditions. Even though there are well-established autonomous navigation solutions, it is worth noting that comprehensive, systematically differentiated examinations of the critical technologies underpinning both single-robot and multi-robot path planning are notably scarce. These technologies encompass aspects such as environmental modeling, criteria for evaluating path quality, the techniques employed in path planning and so on. This paper presents a thorough exploration of techniques within the realm of mobile robot path planning. Initially, we provide an overview of eight diverse methods for mapping, each mirroring the varying levels of abstraction that robots employ to interpret their surroundings. Furthermore, we furnish open-source map datasets suited for both Single-Agent Path Planning (SAPF) and Multi-Agent Path Planning (MAPF) scenarios, accompanied by an analysis of prevalent evaluation metrics for path planning. Subsequently, focusing on the distinctive features of SAPF algorithms, we categorize them into three classes: classical algorithms, intelligent optimization algorithms, and artificial intelligence algorithms. Within the classical algorithms category, we introduce graph search algorithms, random sampling algorithms, and potential field algorithms. In the intelligent optimization algorithms domain, we introduce ant colony optimization, particle swarm optimization, and genetic algorithms. Within the domain of artificial intelligence algorithms, we discuss neural network algorithms and fuzzy logic algorithms. Following this, we delve into the different approaches to MAPF planning, examining centralized planning which emphasizes decoupling conflicts, and distributed planning which prioritizes task execution. Based on these categorizations, we comprehensively compare the characteristics and applicability of both SAPF and MAPF algorithms, while highlighting the challenges that this field is currently grappling with.

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S. Raj, Jennifer. "A COMPREHENSIVE SURVEY ON THE COMPUTATIONAL INTELLIGENCE TECHNIQUES AND ITS APPLICATIONS." Journal of ISMAC 01, no. 03 (2019): 147–59. http://dx.doi.org/10.36548/jismac.2019.3.002.

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The artificial intelligence that tries to imitate the human beings by gathering a vast knowledge gained using the reasoning, planning, searching and prediction fails in certain areas that necessitate a construction of large set of rules. The AI also faces challenges due to the growing demands in the learning and the search optimization. These failures of AI paved a path for the growth of the computational tools that led to the rise of the new regimen that is the computational intelligence. The paper presents the comprehensive survey of the computational intelligent techniques and its applications as they seem to be an effective alternative for the artificial intelligence overcoming the failures and the draw backs in it.

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Nalluri, MadhuSudana Rao, Kannan K., Manisha M., and Diptendu Sinha Roy. "Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization." Journal of Healthcare Engineering 2017 (2017): 1–27. http://dx.doi.org/10.1155/2017/5907264.

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With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.

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Zuo, Yan Hong, and Ke Ren Zhang. "Research on Key Techniques of Intelligent Optimization of Batch Production Job Shop Scheduling." Advanced Materials Research 753-755 (August 2013): 1903–9. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.1903.

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Job-shop scheduling is the very important, but weak part in the integrated manufacturing system. In view of the Job-shop scheduling characteristics for batch production of enterprises, this article analysised batch production scheduling problem in detail; then put out a workshop Intelligent Scheduling Optimization technical framework which contained six-stories. And study the basic theory and key technology of this structure framework in-depth, then proposed object-oriented technology which based on improved tabu search algorithm and NSGA-II algorithm to achieve the intelligent optimization targets , and in the experiment proved it is practical and effective.

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Mishra, Subhashree, Sudhansu Sekhar Singh, Bhabani Shankar Prasad Mishra, and Prabin Kumar Panigrahi. "Research on Soft Computing Techniques for Cognitive Radio." International Journal of Mobile Computing and Multimedia Communications 7, no. 2 (2016): 53–73. http://dx.doi.org/10.4018/ijmcmc.2016040104.

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Presently, the world of wireless communication is going under some crucial challenges, which attracts the attention of several researchers. Cognitive radio is defined as a multidimensional aware, autonomous radio system that learns from its experiences to reason, plan & decide future actions to meet user needs. Such a highly varied radio environment calls on intelligent management, allocation & usage of scarce resources. Issues like spectrum sensing & allocation, environmental learning i.e., adaptability & capability to learn attracts the attention of several soft computing learning & optimization techniques like neural networks, fuzzy logic, genetic algorithm & swarm intelligence. The cognitive engine behind the radio combines the sensing, learning, switching, and optimization algorithms to control & adapt the radio system from the physical layer to the top of the communication stack. This paper presents a critical review on different soft computing approaches applied over the cognitive radio issues & also points out different research directions over it.

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Sharma, Saurabh, Vijay Kumar Gahlawat, Kumar Rahul, Rahul S. Mor, and Mohit Malik. "Sustainable Innovations in the Food Industry through Artificial Intelligence and Big Data Analytics." Logistics 5, no. 4 (2021): 66. http://dx.doi.org/10.3390/logistics5040066.

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The agri-food sector is an endless source of expansion for nourishing a vast population, but there is a considerable need to develop high-standard procedures through intelligent and innovative technologies, such as artificial intelligence (AI) and big data. This paper addresses the research concerning AI and big data analytics in the food industry, including machine learning, artificial neural networks (ANNs), and various algorithms. Logistics, supply chain, marketing, and production patterns are covered along with food sub-sector applications for artificial intelligence techniques. It is found that utilization of AI techniques and the intelligent optimization algorithm also leads to significant process and production management. Thus, digital technologies are a boon for the food industry, where AI and big data have enabled us to achieve optimum results in realtime.

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Dan, Danhui, Tong Yang, and Jiongxin Gong. "Intelligent Platform for Model Updating in a Structural Health Monitoring System." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/628619.

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The main aim of this study is to develop an automated smart software platform to improve the time-consuming and laborious process of model updating. We investigate the key techniques of model updating based on intelligent optimization algorithms, that is, accuracy judgment methods for basic finite element model, parameter choice theory based on sensitivity analysis, commonly used objective functions and their construction methods, particle swarm optimization, and other intelligent optimization algorithms. An intelligent model updating prototype software framework is developed using the commercial software systems ANSYS and MATLAB. A parameterized finite element modeling technique is proposed to suit different bridge types and different model updating requirements. An objective function library is built to fit different updating targets. Finally, two case studies are conducted to verify the feasibility of the techniques used by the proposed software platform.

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