Relevant bibliographies by topics / Whale optimizer algorithm

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Academic literature on the topic 'Whale optimizer algorithm'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Whale optimizer algorithm"

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Abdel-Basset, Mohamed, Reda Mohamed, Karam M. Sallam, and Ripon K. Chakrabortty. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm." Mathematics 10, no. 19 (2022): 3466. http://dx.doi.org/10.3390/math10193466.

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This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is
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Ewees, Ahmed A., Zakariya Yahya Algamal, Laith Abualigah, et al. "A Cox Proportional-Hazards Model Based on an Improved Aquila Optimizer with Whale Optimization Algorithm Operators." Mathematics 10, no. 8 (2022): 1273. http://dx.doi.org/10.3390/math10081273.

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Recently, a new optimizer, called the Aquila Optimizer (AO), was developed to solve different optimization problems. Although the AO has a significant performance in various problems, like other optimization algorithms, the AO suffers from certain limitations in its search mechanism, such as local optima stagnation and convergence speed. This is a general problem that faces almost all optimization problems, which can be solved by enhancing the search process of an optimizer using an assistant search tool, such as using hybridizing with another optimizer or applying other search techniques to b
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Wang, GuoChun, Wenyong Gui, Guoxi Liang, et al. "Spiral Motion Enhanced Elite Whale Optimizer for Global Tasks." Complexity 2021 (August 30, 2021): 1–33. http://dx.doi.org/10.1155/2021/8130378.

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The whale optimization algorithm (WOA) is a high-performance metaheuristic algorithm that can effectively solve many practical problems and broad application prospects. However, the original algorithm has a significant improvement in space in solving speed and precision. It is easy to fall into local optimization when facing complex or high-dimensional problems. To solve these shortcomings, an elite strategy and spiral motion from moth flame optimization are utilized to enhance the original algorithm’s efficiency, called MEWOA. Using these two methods to build a more superior population, MEWOA
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Mehta, Pranav, Betül Sultan Yıldız, Nantiwat Pholdee, et al. "A novel generalized normal distribution optimizer with elite oppositional based learning for optimization of mechanical engineering problems." Materials Testing 65, no. 2 (2023): 210–23. http://dx.doi.org/10.1515/mt-2022-0259.

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Abstract Optimization of engineering discipline problems are quite a challenging task as they carry design parameters and various constraints. Metaheuristic algorithms can able to handle those complex problems and realize the global optimum solution for engineering problems. In this article, a novel generalized normal distribution algorithm that is integrated with elite oppositional-based learning (HGNDO-EOBL) is studied and employed to optimize the design of the eight benchmark engineering functions. Moreover, the statistical results obtained from the HGNDO-EOBL are collated with the data obt
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Euldji, Rafik, Noureddine Batel, Redha Rebhi, et al. "Optimal Backstepping-FOPID Controller Design for Wheeled Mobile Robot." Journal Européen des Systèmes Automatisés​ 55, no. 1 (2022): 97–107. http://dx.doi.org/10.18280/jesa.550110.

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A design of an optimal backstepping fractional order proportional integral derivative (FOPID) controller for handling the trajectory tracking problem of wheeled mobile robots (WMR) is examined in this study. Tuning parameters is a challenging task, to overcome this issue a hybrid meta-heuristic optimization algorithm has been utilized. This evolutionary technique is known as the hybrid whale grey wolf optimizer (HWGO), which benefits from the performances of the two traditional algorithms, the whale optimizer algorithm (WOA) and the grey wolf optimizer (GWO), to obtain the most suitable soluti
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Hemanth, Mangalapuri. "Nature inspired metaheuristic effectiveness used in phishing intrusion detection systems with grey wolf algorithm techniques." i-manager’s Journal on Future Engineering and Technology 20, no. 3 (2025): 23. https://doi.org/10.26634/jfet.20.3.21816.

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Phishing attacks pose a severe cybersecurity threat, often bypassing traditional Intrusion Detection Systems (IDS) due to high false positives and low detection accuracy. This study enhances phishing detection by integrating nature-inspired metaheuristic algorithms with machine learning. Support Vector Machine (SVM) performance is optimized using Grey Wolf Optimizer (GWO), Firefly Algorithm, Bat Algorithm, and Whale Optimization Algorithm, mimicking natural behaviours for improved efficiency. Experimental evaluation shows that our model outperforms traditional methods, achieving over 95% detec
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Baihaqi, Muhammad Aghniya, and Dana Marsetiya Utama. "No-Wait Flowshop Permutation Scheduling Problem : Fire Hawk Optimizer Vs Beluga Whale Optimization Algorithm." Jurnal Ilmiah Teknik Industri 22, no. 1 (2023): 124–36. http://dx.doi.org/10.23917/jiti.v22i1.21128.

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No-Wait Flowshop Permutation Scheduling Problem (NWPFSP) is a scheduling problem that states that every job completed on machine n must be processed immediately on the next machine. The NWPFSP problem is an extension of the flowshop problem. This article proposes two new algorithms fire hawk optimization and beluga whale optimization, to solve the NWPFSP problem and minimize makespan. The two new algorithms developed to solve the NWPFSP problem are tested on three different cases. Each algorithm was run 30 times and was compared using an independent sample t-test. The results were also compare
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Zhai, Q. H., T. Ye, M. X. Huang, S. L. Feng, and H. Li. "Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization." Computational Intelligence and Neuroscience 2020 (August 28, 2020): 1–19. http://dx.doi.org/10.1155/2020/8834162.

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In the field of asset allocation, how to balance the returns of an investment portfolio and its fluctuations is the core issue. Capital asset pricing model, arbitrage pricing theory, and Fama–French three-factor model were used to quantify the price of individual stocks and portfolios. Based on the second-order stochastic dominance rule, the higher moments of return series, the Shannon entropy, and some other actual investment constraints, we construct a multiconstraint portfolio optimization model, aiming at comprehensively weighting the returns and risk of portfolios rather than blindly maxi
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Hudaib, Amjad A., and Hussam N. Fakhouri. "Supernova Optimizer: A Novel Natural Inspired Meta-Heuristic." Modern Applied Science 12, no. 1 (2017): 32. http://dx.doi.org/10.5539/mas.v12n1p32.

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Bio and natural phenomena inspired algorithms and meta-heuristics provide solutions to solve optimization and preliminary convergence problems. It significantly has wide effect that is integrated in many scientific fields. Thereby justifying the relevance development of many applications that relay on optimization algorithms, which allow finding the best solution in the shortest possible time. Therefore it is necessary to further consider and develop new swarm intelligence optimization algorithms. This paper proposes a novel optimization algorithm called supernova optimizer (SO) inspired by th
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Sheng, Long, Sen Wu, and Zongyu Lv. "Modified Grey Wolf Optimizer and Application in Parameter Optimization of PI Controller." Applied Sciences 15, no. 8 (2025): 4530. https://doi.org/10.3390/app15084530.

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The Grey Wolf Optimizer (GWO) is a well-known metaheuristic algorithm that currently has an extremely wide range of applications. However, with the increasing demand for accuracy, its shortcomings of low exploratory and population diversity are increasingly exposed. A modified Grey Wolf Optimizer (M-GWO) is proposed to tackle these weaknesses of the GWO. The M-GWO introduces mutation operators and different location-update strategies, achieving a balance between exploration and development. The experiment validated the performance of the M-GWO using the CEC2017 benchmark function and compared
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Books on the topic "Whale optimizer algorithm"

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Nolan, Jerry P. Advanced life support. Edited by Neil Soni and Jonathan G. Hardman. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0091.

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Anaesthetists have a central role in cardiopulmonary resuscitation (CPR). The incidence of treated out-of-hospital cardiopulmonary arrest is 40 per 100 000 population and is associated with a survival rate to hospital discharge of 8–10%. The incidence of in-hospital cardiac arrest (IHCA) is 1–5 per 1000 admissions and is associated with a survival rate to hospital discharge of 13–17%. The most effective strategy for reducing mortality from IHCA is to prevent it occurring by detecting and treating those at risk or to identify in advance those with no chance of survival and to make a decision no
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Book chapters on the topic "Whale optimizer algorithm"

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Mirjalili, Seyed Mohammad, Seyedeh Zahra Mirjalili, Nima Khodadadi, Vaclav Snasel, and Seyedali Mirjalili. "Grey Wolf Optimizer, Whale Optimization Algorithm, and Moth Flame Optimization for Optimizing Photonics Crystals." In Studies in Computational Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09835-2_9.

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Mirjalili, Seyedehzahra, Seyed Mohammad Mirjalili, Shahrzad Saremi, and Seyedali Mirjalili. "Whale Optimization Algorithm: Theory, Literature Review, and Application in Designing Photonic Crystal Filters." In Nature-Inspired Optimizers. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12127-3_13.

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Albert, Elvira, Pablo Gordillo, Alejandro Hernández-Cerezo, and Albert Rubio. "A Max-SMT Superoptimizer for EVM handling Memory and Storage." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99524-9_11.

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AbstractSuperoptimization is a compilation technique that searches for the optimal sequence of instructions semantically equivalent to a given (loop-free) initial sequence. With the advent of SMT solvers, it has been successfully applied to LLVM code (to reduce the number of instructions) and to Ethereum EVM bytecode (to reduce its gas consumption). Both applications, when proven practical, have left out memory operations and thus missed important optimization opportunities. A main challenge to superoptimization today is handling memory operations while remaining scalable. We present $$\textsf {GASOL}^{v2}$$ GASOL v 2 , a gas and bytes-size superoptimization tool for Ethereum smart contracts, that leverages a previous Max-SMT approach for only stack optimization to optimize also wrt. memory and storage. $$\textsf {GASOL}^{v2}$$ GASOL v 2 can be used to optimize the size in bytes, aligned with the optimization criterion used by the Solidity compiler , and it can also be used to optimize gas consumption. Our experiments on 12,378 blocks from 30 randomly selected real contracts achieve gains of 16.42% in gas wrt. the previous version of the optimizer without memory handling, and gains of 3.28% in bytes-size over code already optimized by .
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Zhang, Ziqi, Hongfa Ding, Shuochun Yu, and Zhou He. "An Intelligent Optimization Method for Transcranial Magnetic Stimulation Waveforms to Improve Stimulation Selectivity." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4856-6_4.

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Abstract Transcranial Magnetic Stimulation (TMS) is a non-invasive neuromodulation technique that stimulates the brain using an induced electric field generated by a stimulation coil and pulsed current. The precision of this stimulation largely determines its effectiveness. This paper proposes an intelligent waveform optimization method that utilizes intelligent algorithms combined with real brain structures to optimize stimulation waveforms, innovatively improving stimulation accuracy from the temporal scale while also guiding the construction of TMS circuits. First, a selectivity index is established based on brain structure and neuron morphology. Then, particle swarm optimization is employed to identify waveform parameters that optimize the selectivity index, followed by the construction of an experimental platform to implement the optimized waveforms. The optimized waveforms significantly improve stimulation selectivity, i.e., precision, and the use of intelligent algorithms offers new possibilities for developing personalized treatment plans. Additionally, the insights gained from the waveform effects during the optimization process lay the groundwork for further waveform and circuit optimization.
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Sudha, I., P. S. Ramesh, S. Jagadeesan, Guru Vimal Kumar Murugan, C. Gokulnath, and N. Poongavanam. "Jaya integrated binary whale optimized algorithm for preserving medical data." In Artificial Intelligence, Blockchain, Computing and Security Volume 2. CRC Press, 2023. http://dx.doi.org/10.1201/9781032684994-49.

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van den Haak, Lars B., Anton Wijs, Marieke Huisman, and Mark van den Brand. "$${\textsc {HaliVer}}$$: Deductive Verification and Scheduling Languages Join Forces." In Tools and Algorithms for the Construction and Analysis of Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57256-2_4.

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AbstractThe HaliVer tool integrates deductive verification into the popular scheduling language Halide, used for image processing pipelines and array computations. HaliVer uses VerCors, a separation logic-based verifier, to verify the correctness of (1) the Halide algorithms and (2) the optimised parallel code produced by Halide when an optimisation schedule is applied to an algorithm. This allows proving complex, optimised code correct while reducing the effort to provide the required verification annotations. For both approaches, the same specification is used. We evaluated the tool on several optimised programs generated from characteristic Halide algorithms, using all but one of the essential scheduling directives available in Halide. Without annotation effort, HaliVer proves memory safety in almost all programs. With annotations HaliVer, additionally, proves functional correctness properties. We show that the approach is viable and reduces the manual annotation effort by an order of magnitude.
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Schröder, Calvin, Jan Niklas van Detten, and Sander J. J. Leemans. "Locally Optimized Process Tree Discovery." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82225-4_29.

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Abstract Business process optimization typically involves discovering models that are fit, precise, sound and simple. Process discovery algorithms automatically obtain these models from event logs, records of past process executions, enabling insights into the underlying process. However, event logs often contain incomplete and infrequent behaviour, which presents significant challenges for these algorithms. To address these issues, we propose a new process discovery technique called OptIMIIst, which guarantees soundness while handling both infrequent and incomplete behaviour and discovering locally optimal process trees. This technique, based on the Inductive Miner framework, operates in two steps. First, it creates candidate mining decisions for each process tree operator and then decides on the optimal decision through a local fitness and precision estimation. An experimental evaluation demonstrates that OptIMIIst produces high-quality process models and offers competitive fitness, precision, and simplicity compared to state-of-the-art techniques, while maintaining soundness.
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Ramponi, Giorgia. "Learning in the Presence of Multiple Agents." In Special Topics in Information Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15374-7_8.

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AbstractReinforcement Learning (RL) has emerged as a powerful tool to solve sequential decision-making problems, where a learning agent interacts with an unknown environment in order to maximize its rewards. Although most RL real-world applications involve multiple agents, the Multi-Agent Reinforcement Learning (MARL) framework is still poorly understood from a theoretical point of view. In this manuscript, we take a step toward solving this problem, providing theoretically sound algorithms for three RL sub-problems with multiple agents: Inverse Reinforcement Learning (IRL), online learning in MARL, and policy optimization in MARL. We start by considering the IRL problem, providing novel algorithms in two different settings: the first considers how to recover and cluster the intentions of a set of agents given demonstrations of near-optimal behavior; the second aims at inferring the reward function optimized by an agent while observing its actual learning process. Then, we consider online learning in MARL. We showed how the presence of other agents can increase the hardness of the problem while proposing statistically efficient algorithms in two settings: Non-cooperative Configurable Markov Decision Processes and Turn-based Markov Games. As the third sub-problem, we study MARL from an optimization viewpoint, showing the difficulties that arise from multiple function optimization problems and providing a novel algorithm for this scenario.
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Schneider, Lennart, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann, and Pascal Kerschke. "HPO $$\times $$ ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14714-2_40.

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AbstractHyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been proposed over the last years, little progress has been made in illuminating and examining the actual structure of these black-box optimization problems. Exploratory landscape analysis (ELA) subsumes a set of techniques that can be used to gain knowledge about properties of unknown optimization problems. In this paper, we evaluate the performance of five different black-box optimizers on 30 HPO problems, which consist of two-, three- and five-dimensional continuous search spaces of the XGBoost learner trained on 10 different data sets. This is contrasted with the performance of the same optimizers evaluated on 360 problem instances from the black-box optimization benchmark (BBOB). We then compute ELA features on the HPO and BBOB problems and examine similarities and differences. A cluster analysis of the HPO and BBOB problems in ELA feature space allows us to identify how the HPO problems compare to the BBOB problems on a structural meta-level. We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems. We highlight open challenges of ELA for HPO and discuss potential directions of future research and applications.
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Chen, Li-an, Mengqian Guo, Yongxin Jiang, Xi Chen, and Yiping Chen. "Identification Method of VT Ferroresonance Fault Based on Improved CEEMD and WOA-RF." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4856-6_10.

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Abstract In order to solve the shortcoming of the low accuracy of ferroresonance fault identification of voltage transformers (VT) by the traditional microcomputer harmonic suppression device, the T-test and variance contribution rate were introduced to form an improved complete ensemble empirical mode decomposition (ICEEMD) method, and an identification method of VT ferroresonance fault based on ICEEMD feature extraction, kernel principal components analysis (KPCA) feature reduction, and random forest (RF) construction optimized by the whale optimization algorithm (WOA) was proposed. Firstly, taking the 10 kV neutral point non-grounded system as an example, the zero-sequence voltage signals under different working conditions of the distribution network are taken as input samples. Secondly, the ICEEMD was used to extract the feature of the signal, and the KPCA was used to reduce the feature dimension to form the sample set with the best feature dimension. Finally, the WOA was used to optimize the RF parameters to determine the optimal parameters, built a VT ferroresonance fault identification model, and classify the sample set. Experimental results show that the ICEEMD can effectively extract zero-sequence voltage signal features under different working states, and the accuracy of the proposed fault identification method reaches 98.33%.
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Conference papers on the topic "Whale optimizer algorithm"

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Eid, Mahmoud M., Abdelatif H. Abouali, and Kamal ElDahshan. "Enhancing Data Quality in Smart Video Surveillance Systems: A Whale Optimizer-Based Imputation Algorithm." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652650.

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Wang, Hua. "Intelligent Prediction and Training Optimization of Sports using Enhanced Whale Optimized Artificial Neural Network." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721754.

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Nazirov, Mukhtor, and Nuriddin Qolqanov. "The Optimized Algorithm Creation for the Whole HE System." In 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2024. http://dx.doi.org/10.1109/icacite60783.2024.10616409.

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Tiwari, Raj Gaurang, Satvik Vats, and Ambuj Kumar Agarwal. "Enhanced Sugarcane Leaf Disease Detection via Hybrid Deep Capsule Autoencoder CNN Optimized by Improved Whale Algorithm." In 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2024. https://doi.org/10.1109/cicn63059.2024.10847320.

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Peng, Yuanxia. "Research on the Whole Vehicle Intermodal Transport Model Based on Optimized Adaptive Genetic Algorithm." In 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA). IEEE, 2024. http://dx.doi.org/10.1109/icipca61593.2024.10709002.

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Nalcaci, Gamze, and Muammer Ermis. "Selective harmonic elimination for three-phase voltage source inverters using whale optimizer algorithm." In 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE). IEEE, 2018. http://dx.doi.org/10.1109/iceee2.2018.8391290.

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Tahernezhad-Javazm, Farajollah, Debbie Rankin, and Damien Coyle. "R2-HMEWO: Hybrid multi-objective evolutionary algorithm based on the Equilibrium Optimizer and Whale Optimization Algorithm." In 2022 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2022. http://dx.doi.org/10.1109/cec55065.2022.9870371.

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Khelifi, Elmabrouk, Mohamed Nadjib Brahami, Benalia M’Hamdi, Fatima Zohra Boudjella, Imen Souhila Bousmaha, and Yassine Bouroumeid. "Power Flow Optimization of the Adrar Power System in Algeria Using the Whale Optimizer Algorithm." In 2023 International Conference on Electrical Engineering and Advanced Technology (ICEEAT). IEEE, 2023. http://dx.doi.org/10.1109/iceeat60471.2023.10426080.

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Jokić, Aleksandar, Milica Petrović, and Zoran Miljković. "Integrated process planning and scheduling of production systems based on mountain gazelle optimizer." In XIX International May Conference on Strategic Management – IMCSM24 Proceedings. University of Belgrade, Technical Faculty in Bor, 2024. http://dx.doi.org/10.5937/imcsm24014j.

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The mass customization paradigm, in conjunction with high market demands, puts a significant burden on contemporary production systems to output a larger quantity of diversified parts. Consequently, production systems need to achieve even higher flexibility levels through physical and functional reconfigurability. One way of achieving these high levels of flexibility is by utilizing optimization of both scheduling and process planning. In this paper, the authors propose to solve an NP-hard integrated process planning and scheduling optimization problem with transportation constraints regarding
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Bousmaha, Rabab. "A novel hybrid Aquila optimizer with Whale optimziation algorithm for global optimization, feature selection and optimizing SVM parameters." In 2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS). IEEE, 2022. http://dx.doi.org/10.1109/pais56586.2022.9946891.

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Reports on the topic "Whale optimizer algorithm"

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Li, Yan, Yuhao Luo, and Xin Lu. PHEV Energy Management Optimization Based on Multi-Island Genetic Algorithm. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0739.

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The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PH
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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Paule, Bernard, Flourentzos Flourentzou, Tristan de KERCHOVE d’EXAERDE, Julien BOUTILLIER, and Nicolo Ferrari. PRELUDE Roadmap for Building Renovation: set of rules for renovation actions to optimize building energy performance. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541614638.

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In the context of climate change and the environmental and energy constraints we face, it is essential to develop methods to encourage the implementation of efficient solutions for building renovation. One of the objectives of the European PRELUDE project [1] is to develop a "Building Renovation Roadmap"(BRR) aimed at facilitating decision-making to foster the most efficient refurbishment actions, the implementation of innovative solutions and the promotion of renewable energy sources in the renovation process of existing buildings. In this context, Estia is working on the development of infer
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Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7580664.bard.

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Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real tim
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