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An interdependent evolutionary machine learning model applied to global horizontal irradiance modeling

NEURAL COMPUTING & APPLICATIONS 2023年第16期35卷 12099-12120页

作者： Alves Basilio, Samuel da Costa Saporetti, Camila M. Goliatt, Leonardo Univ Fed Juiz de Fora Dept Appl & Computat Mech Juiz De Fora MG Brazil Fed Ctr Technol Educ Minas Gerais Dept Comp & Mech Leopoldina MG Brazil Univ Estado Rio De Janeiro Polytech Inst Dept Computat Modeling Nova Friburgo RJ Brazil

The most important input parameter in all solar power generation forecasting systems is solar radiation. Your estimation is necessary for the development of any photovoltaic system project. However, this estimate depends on expensive devices, namely pyranometers and pyrheliometers. Therefore, predicting such values through mathematical and computational models is an attractive approach where costs can be reduced. In particular, machine learning methods have been widely successfully applied to this task. The efficiency of a machine learning model depends on a suitable set of parameters. Evolutionary algorithms are helpful and widely used to optimize internal parameters and select the most relevant variables. In this context, machine learning models use evolutionary algorithms' search capability to improve forecasting performance. This work presents a study incorporating different evolutionary algorithms for parameter adjustment in machine learning models applied to solar radiation prediction. Two years of observation data from the Dar es Salaam weather station in Tanzania were used. The results show the presented framework's applicability to finding the best subset of variables, machine learning model and optimization algorithm combination. Although promising results have been obtained in the experiments, it should be clear that care must be taken to generalize the conclusions. The integration of machine learning model with optimization algorithms is limited to a defined data collection context, under specific local environmental conditions and only under data collection from a meteorological station.

关键词： Solar radiation Predictive models optimization algorithms Feature selection Hybrid approach

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A new NEST-IGWO strategy for determining optimal IGWO control parameters

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NEURAL COMPUTING & APPLICATIONS 2023年第20期35卷 15143-15165页

作者： Rabie, Asmaa H. Eltamaly, Ali M. Mansoura Univ Comp & Control Dept Fac Engn Mansoura Egypt Mansoura Univ Elect Engn Dept Mansoura Egypt King Saud Univ Sustainable Energy Technol Ctr Riyadh 11421 Saudi Arabia King Saud Univ Saudi Elect Co Chair Power Syst Reliabil & Secur Riyadh 11421 Saudi Arabia

Online optimization applications require fast convergence without sacrificing accuracy. Although the gray wolf optimization (GWO) algorithm is showing good convergence performance, it still needs further improvement to achieve these requirements. Optimal determination of the GWO control parameters can substantially improve the converge performance. All studies in the literature introduced efforts in tuning these parameters on try-and-error bases which may not satisfy the requirements of the online applications. For this reason, a novel nested improved GWO (NEST-IGWO) is used to determine the optimal control parameters for the IGWO. This novel strategy substantially improved the convergence time and accuracy, especially with online control systems. This strategy is having two nested IGWO loops. The internal IGWO loop includes the target function needed to be optimized. Meanwhile, the external loop is used to optimally determine the control parameters of the internal one. The objective function of the external loop is the failure rate and convergence time of the internal one. The results obtained from the NEST-IGWO are compared to 10 existing optimization algorithms for 10 different benchmark functions. Moreover, these optimization algorithms were applied to determine the parameters of the PV-cell model as a real-world application. The results showed that NEST-IGWO outperformed the other 10 optimization algorithms for all benchmark functions understudy and the estimations of the PV-cell parameters in terms of failure rate and convergence time. With the use of the NEST-IGWO, the convergence time is reduced by 90% of the average convergence time for all other algorithms. Moreover, the failure rate is reduced to 0% which is not the case for other algorithms understudy. These outstanding results prove the superiority of the NEST-IGWO compared to the other algorithms, and it opens a new venue for determining optimal control parameters for all optimization algorithms.

关键词： optimization algorithms Gray wolf optimization Optimal control parameters Convergence time Failure rate NEST-IGWO PV solar cells Photovoltaic

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Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

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IEEE ACCESS 2023年 11卷 16907-16922页

作者： Ali, Amira Noor Farhanie Sulaima, Mohamad Fani Razak, Intan Azmira Wan Abdul Kadir, Aida Fazliana Abdul Mokhlis, Hazlie Univ Teknikal Malaysia Melaka Fac Elect Engn Melaka 76100 Malaysia Univ Malaya Fac Engn Kuala Lumpur 50603 Malaysia

In recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large-scale data and the frequent requirement for near-real-time decisions mean that Artificial Intelligence (AI) has recently emerged as a key technology for enabling DSM. optimization algorithm methods can be used to address a variety of problems, including selecting the optimal set of consumers to respond to, learning their attributes and preferences, dynamic pricing, device scheduling, and control, as well as determining the most effective way to incentive and reward participants in DR schemes fairly and effectively. The implementation optimization algorithm needs proper selection to mitigate the cost of energy consumption. Due to that reason, this paper outlines various challenges and opportunities in developing, utilizing, controlling, and scheduling the DR scheme's optimization algorithm. In addition, several issues in applications and advantages of optimization techniques in artificial intelligence approaches are discussed. The importance of implementing demand response mechanisms in developing countries is also presented. In addition, the status of demand response optimization in demand-side management solutions is also illustrated congruently.

关键词： Demand response optimization Machine learning Job shop scheduling Task analysis Support vector machines Pricing Artificial intelligence (AI) demand response (DR) demand side management (DSM) optimization algorithms

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Assessment of the fracture energy of concrete applying hybridized support vector regression analysis

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 263-279页

作者： Suyuan, Yin Jingtao, Li Jing, Wang Hebei Vocat Coll Rail Transportat Shijiazhuang 050000 Peoples R China

Considering literature and developed models, regression approaches were performed to estimate the concrete initial (G( f)) and total (G(F)) fracture energy employing mechanical characteristics and mixed design factors. In all, 264 samples from previous studies were recovered. Therefore, to get a better concept of G (f) and G(F) of concrete, this research combined support vector regression (SVR) analysis with the equilibrium optimizer (EO) and grey wolf optimization (GWO) techniques to produce hybridized SVR analysis. The calculation and analysis, by considering six criteria (i.e., R , RMSE, RAE, RRSE, SSE and PI) for G( f) and G(F) depict that both optimized EO - SVR and GWO - SVR regression analysis could remarkably perform desirable performance during the prediction procedure. The outperformed SVR analysis was compared with the literature, where the created EO - SVR regression also provides a reasonable enhancement in the effectiveness, with an improvement of all metrics. Overall, although GWO - SVR system has its own ability in the prediction process, based on the justifications and workability of the models, it seems that the EO - SVR analysis is very reliable for determining concrete G( f) and G(F).

关键词： Initial fracture energy Total fracture energy Concrete Support vector regression optimization algorithms

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Submodular Maximization With Limited Function Access

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第9期68卷 5522-5535页

作者： Downie, Andrew Gharesifard, Bahman Smith, Stephen L. Univ Waterloo Elect & Comp Engn Waterloo ON N2L 3G1 Canada Univ Calif Los Angeles Dept Elect & Comp Engn K7L3N6 Los Angeles CA USA

In this article, we consider a class of submodular maximization problems in which decision-makers have limited access to the objective function. We explore scenarios where the decision-maker can observe only pairwise information, i.e., can evaluate the objective function on sets of size two. We begin with a negative result that no algorithm using only k-wise information can guarantee performance better than k/n. We present two algorithms that utilize only pairwise information about the function and characterize their performance relative to the optimal, which depends on the curvature of the submodular function. Additionally, if the submodular function possess a property called supermodularity of conditioning, then we can provide a method to bound the performance based purely on pairwise information. The proposed algorithms offer significant computational speedups over a traditional greedy strategy. A by-product of our study is the introduction of two new notions of curvature, the $k$-Marginal Curvature and the k-Cardinality Curvature. Finally, we present experiments highlighting the performance of our proposed algorithms in terms of approximation and time complexity.

关键词： Autonomous systems optimization algorithms sensor networks submodular maximization

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A Julia Package for Polyhedral Omega and Applications

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ACM Communications in Computer Algebra 2025年第2期58卷 39-42页

作者： Ayylldlz, Tülay Demirel, Deniz Naz Tapan, Ismail Zafeirakopoulos, Zafeirakis Department of Computer Engineering Gebze Technical University Gebze Kocaeli Turkey Department of Philosophy Middle East Technical University Ankara Turkey Section of Mathematics University of Geneva Geneva Switzerland

This paper introduces a Julia package for tackling linear Diophantine systems and related optimization problems using the Polyhedral Omega algorithm. The package integrates partition analysis and polyhedral geometry to offer efficient solutions. *** package implements the Polyhedral Omega algorithm, and *** provides optimization enhancements. © 2025 Copyright is held by the owner/author(s).

关键词： optimization algorithms

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A Dual Averaging Algorithm for Online Modeling of Infinite Memory Nonlinear Systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第9期68卷 5677-5684页

作者： Lagosz, Szymon Wachel, Pawel Sliwinski, Przemyslaw Wroclaw Univ Sci & Technol Dept Control Syst & Mechatron PL-50372 Wroclaw Poland

An online modeling algorithm is derived from a generic stochastic dual averaging (DA) method. It employs a negative entropy as a distance-generating function and the Volterra series expansion as a dictionary. Assuming that the measurement data are not i.i.d. but generated by a nonlinear dynamical system with an infinite, exponentially fading memory, the error bounds are established for both the generic DA method and for the proposed modeling algorithm. The experiments performed on a set of benchmark systems confirm the applicability of the algorithm in real-world scenarios and demonstrate its low computational complexity.

关键词： Nonlinear systems optimization algorithms stochastic systems Volterra series

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Optimized deep learning vision system for human action recognition from drone images

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第1期83卷 1143-1164页

作者： Samma, Hussein Bin Sama, Ali Salem King Fahd Univ Petr & Minerals SDAIA KFUPM Joint Res Ctr Artificial Intelligence Dhahran Saudi Arabia King Faisal Univ Coll Business Adm Dept Management Informat Syst MIS Al Hasa Eastern Provinc Saudi Arabia

There are several benefits to constructing a lightweight vision system that is implemented directly on limited hardware devices. Most deep learning-based computer vision systems, such as YOLO (You Only Look Once), use computationally expensive backbone feature extractor networks, such as ResNet and Inception network. To address the issue of network complexity, researchers created SqueezeNet, an alternative compressed and diminutive network. However, SqueezeNet was trained to recognize 1000 unique objects as a broad classification system. This work integrates a two-layer particle swarm optimizer (TLPSO) into YOLO to reduce the contribution of SqueezeNet convolutional filters that have contributed less to human action recognition. In short, this work introduces a lightweight vision system with an optimized SqueezeNet backbone feature extraction network. Secondly, it does so without sacrificing accuracy. This is because that the high-dimensional SqueezeNet convolutional filter selection is supported by the efficient TLPSO algorithm. The proposed vision system has been used to the recognition of human behaviors from drone-mounted camera images. This study focused on two separate motions, namely walking and running. As a consequence, a total of 300 pictures were taken at various places, angles, and weather conditions, with 100 shots capturing running and 200 images capturing walking. The TLPSO technique lowered SqueezeNet's convolutional filters by 52%, resulting in a sevenfold boost in detection speed. With an F1 score of 94.65% and an inference time of 0.061 milliseconds, the suggested system beat earlier vision systems in terms of human recognition from drone-based photographs. In addition, the performance assessment of TLPSO in comparison to other related optimizers found that TLPSO had a better convergence curve and achieved a higher fitness value. In statistical comparisons, TLPSO surpassed PSO and RLMPSO by a wide margin.

关键词： Deep learning Human action recognition YOLO SqueezeNet optimization algorithms

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Stacking ensemble approach in data mining methods for landslide prediction

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JOURNAL OF SUPERCOMPUTING 2023年第8期79卷 8583-8610页

作者： Abdollahizad, Solmaz Balafar, Mohammad Ali Feizizadeh, Bakhtiar Sangar, Amin Babazadeh Samadzamini, Karim Islamic Azad Univ Dept IT & Comp Engn Urmia Branch Orumiyeh *** Iran Univ Tabriz Fac Elect & Comp Engn Tabriz *** Iran Univ Tabriz Dept Remote Sensing & GIS Tabriz *** Iran Univ Coll Nabi Akram Dept Comp Engn Tabriz *** Iran

Landslide susceptibility mapping is still an ongoing requirement for variety of applications such as land use management plans. The central objective of the present research was to investigate the effect of using ensemble machine learning methods for developing accurate landslide prediction. We aimed to explore and compare three techniques, namely the random forests, support vector machine and multiple-layer neural networks with an adaptive neuro-fuzzy inference system, which incorporates three metaheuristic methods including grey wolf optimization, particle swarm optimization, and shuffled frog leaping algorithm for landslide susceptibility assessment in the East Azerbaijan of Iran. Also, two ensemble ways (voting and stacking) were used in final decision stage. A sum of 766 locations with landslide inventory was recognized in the context of the study. Then the all models were trained using tenfold cross-validation technique. Lastly, the receiver operating characteristic and statistical procedures were employed to validate and contrast the predictive capability of the models. The findings of the study show the ANFIS-PSO model had high performance on the validation dataset (AUC = 0.89). Besides, the study revealed that using stacking ensemble technique could increase the predicting capability of all models (AUC = 0.911).

关键词： Stacking ensemble Adaptive neuro-fuzzy inference system Machine learning optimization algorithms

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A Markovian Incremental Stochastic Subgradient Algorithm

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第1期68卷 124-139页

作者： Massambone, Rafael Costa, Eduardo Fontoura Helou, Elias Salomao Demeter Agrosolut BR-04536001 Sao Paulo Brazil Univ Sao Paulo Inst Ciencias Math & Computacao BR-13560970 Sao Carlos Brazil

In this article, a stochastic incremental subgradient algorithm for the minimization of a sum of convex functions is introduced. The method sequentially uses partial subgradient information, and the sequence of partial subgradients is determined by a general Markov chain. This makes it suitable to be used in networks, where the path of information flow is stochastically selected. We prove convergence of the algorithm to a weighted objective function, where the weights are given by the Cesaro limiting probability distribution of the Markov chain. Unlike previous works in the literature, the Cesaro limiting distribution is general (not necessarily uniform), allowing for general weighted objective functions and flexibility in the method.

关键词： Incremental subgradient algorithms optimization optimization algorithms randomized algorithms

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