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Applications of Advanced Optimized Neuro Fuzzy Models for Enhancing Daily Suspended Sediment Load Prediction

Computer Modeling in Engineering & Sciences 2025年第4期143卷 1249-1272页

作者： Rana Muhammad Adnan Mo Wang Adil Masood Ozgur Kisi Shamsuddin Shahid Mohammad Zounemat-Kermani College of Architecture and Urban Planning Guangzhou UniversityGuangzhou510006China Center for Global Health Research Saveetha Institute of Medical and Technical SciencesChennai600001India Department of Natural and Applied Sciences TERI School of Advanced StudiesNew Delhi110070India Department of Civil Engineering Lübeck University of Applied ScienceLübeck23562Germany Department of Civil Engineering School of TechnologyIlia State UniversityTbilisi0162Georgia School of Civil Environmental and Architectural EngineeringKorea UniversitySeoul02841Republic of Korea School of Civil Engineering Faculty of EngineeringUniversiti Teknologi Malaysia(UTM)Johor Bahru81310Malaysia Department of Civil Engineering Shahid Bahonar University of KermanKerman***Iran

Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental ***,SSL prediction is highly complex due to its nonlinear and dynamic nature,making traditional empirical models *** study proposes a novel hybrid approach,integrating the Adaptive Neuro-Fuzzy Inference System(ANFIS)with the gradient-based optimizer(GBO),to enhance SSL forecasting *** research compares the performance of ANFIS-GBO with three alternative models:standard ANFIS,ANFIS with Particle Swarm Optimization(ANFIS-PSO),and ANFIS with Grey Wolf Optimization(ANFIS-GWO).Historical SSL and streamflow data from the Bailong River Basin,China,are used to train and validate the *** input selection process is optimized using the Multivariate Adaptive Regression Splines(MARS)*** performance is evaluated using statistical metrics such as Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),Nash Sutcliffe Efficiency(NSE),and Determination Coefficient(R^(2)).Additionally,visual assessments,including scatter plots,Taylor diagrams,and violin plots,provide further insights into model *** results indicate that including historical SSL data improves predictive accuracy,with ANFIS-GBO outperforming the other ***-GBO achieves the lowest RMSE and MAE and the highest NSE and R^(2),demonstrating its superior learning ability and *** findings highlight the effectiveness of nature-inspired optimization algorithms in enhancing sediment load forecasting and contribute to the advancement of AI-based hydrological *** research should explore the integration of additional environmental and climatic variables to enhance predictive capabilities further.

关键词： Suspended sediment load prediction neuro-fuzzy gradient-based optimizer ANFIS

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Developing a machine learning-based model using optimization algorithms to estimate the released synthetic gas and solid yield in the gasification process

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2025年第6期8卷 1-20页

作者： Wang, Xiaohe Changchun Coll Elect Technol Dept Gen Educ Changchun 130114 Jilin Peoples R China

Gasification is an important procedure because it efficiently transforms a variety of carbon-containing substances like coal, biomass, and waste into valuable products such as syngas, hydrogen, and synthetic fuels. The effectiveness of the gasification procedure relies on important factors that need to be taken into account. Thus, in the present study, a machine learning (ML) technique was used based on initial data from 312 experiments in order to predict the production rate of raw gases and solids in the gasification process. Over the modeling stage, as the novelty of the research, metaheuristic optimization algorithms as successors were coupled to the Decision Tree (DT) as the main model to tune the performance of the model, leading to simulate gasification efficiently compared to the single model. Raw gases and solids generated at the end of gasification depend on various factors, including input materials and thermodynamic parameters, e.g., temperature. By applying the developed hybrid models with optimizers COOT optimization algorithm (COA) and gradient-based optimizer (GBO), it becomes feasible to create a framework quantifying the influence of the variables on the production of gases and solids. Several assessing criteria were employed to evaluate the performance of developed models. By investigating the results of modeling, hybrid models were found at a higher level of modeling in terms of coefficient of determination with the average calculated R2 of around 98%, while for a single model of 96%. Generally, the developing approach of the bare single AI-based models can be used to simulate thermodynamic operations to give experts more understanding of processes working efficiently.

关键词： Gasification process Biomass Energy production Biochar yield Decision tree Coot optimization algorithm gradient-based optimizer

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Convergence of Sharpness-Aware Minimization with Momentum 2nd

Convergence of Sharpness-Aware Minimization with Momentum

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2nd International Conference on Information Technologies and Their Applications

作者： Pham Duy Khanh Luong, Hoang-Chau Mordukhovich, Boris S. Dat Ba Tran Truc Vo HCM Univ Educ Dept Math Ho Chi Minh City Vietnam VNU HCM Univ Sci Dept Comp Sci Ho Chi Minh City Vietnam Wayne State Univ Dept Math Detroit MI 48202 USA Wayne State Univ Dept Econ Detroit MI USA

ISBN: (纸本)9783031734199;9783031734205

This paper presents an analysis of Sharpness-Aware Minimization (SAM), a recently introduced efficient optimizer that has demonstrated remarkable improvements in the generalization of deep neural networks. A comprehensive analysis of the asymptotic convergence behaviors of the optimizer when integrated with momentum is proposed. We first show the convergence of the gradient sequence to zero and the topological properties of the set of accumulation points generated by the iterative sequence. Under the assumption of the isolation of stationary points, especially when the function is strongly convex, the convergence of the sequence of iterates is ensured. To validate the practical implications of our analysis, we conduct numerical experiments on classification tasks employing well-known deep learning models, including ResNet18 and ResNet34, with standard datasets CIFAR-10, CIFAR-100, MNIST, and Fashion-MNIST. The numerical results show that, in general, incorporating momentum improves both the training process and testing accuracy for SAM rather than just using standard SGD.

关键词： Deep Learning gradient-based optimizer Momentum Acceleration Neural Networks Sharpness-Aware Minimization

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Intelligent Joint Optimization of Deployment and Task Scheduling for Mobile Users in Multi-UAV-Assisted MEC System

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2025年第1期2025卷

作者： Abdel-Basset, Mohamed Mohamed, Reda Salam, Amira Sallam, Karam M. Hezam, Ibrahim M. Radwan, Ibrahim Zagazig Univ Fac Comp & Informat Dept Comp Sci Zagazig 44519 Egypt Univ Sharjah Comp Sci Dept Sharjah U Arab Emirates King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia Univ Canberra Fac Sci & Technol Dept AI & Robot Canberra ACT Australia

Mobile edge computing (MEC) servers integrated with multi-unmanned aerial vehicles (multi-UAVs) present a new system the multi-UAV-assisted MEC system. This system relies on the mobility of the UAVs to reduce the transmission distance between the servers and mobile users, thereby enhancing service quality and minimizing the overall energy consumption. Achieving optimal UAV deployment and precise task scheduling is crucial for improved coverage and service quality in this system. This problem is framed as a nonconvex optimization problem known as joint task scheduling and deployment optimization. Recently, an optimization technique based on a dual-layer framework: Upper layer optimization and lower layer optimization have been proposed to tackle this problem and achieved superior performance compared to the alternative methods. In this framework, the lower layer was responsible for task scheduling optimization, while the upper layer was designed to assist in optimizing UAV deployment and thus achieving improved coverage and enhanced task scheduling for mobile users, thereby minimizing the total energy consumption. However, further refinement of upper layer optimization is needed to improve the deployment process. In this study, the upper layer undergoes enhancement through key modifications: First, random selection of the solutions is replaced with sequential selection to maintain the unique characteristics of each individual throughout the optimization process, fostering both exploration and exploitation. Second, a selection of recently reported metaheuristic algorithms, such as spider wasp optimizer (SWO), generalized normal distribution optimization (GNDO), and gradient-based optimizer (GBO), are adapted to optimize UAV deployments. Both improved upper layer and lower layer optimization led to the development of novel, more effective optimization approaches, including IToGBOTaS, IToGNDOTaS, and IToSWOTaS. These techniques are evaluated using nine instances with a

关键词： deployment optimization gradient-based optimizer improved upper layer optimization mobile edge computing multi-UAV task scheduling optimization

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Evolution-based energy-efficient data collection system for UAV-supported IoT: Differential evolution with population size optimization mechanism

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 245卷

作者： Abdel-Basset, Mohamed Mohamed, Reda Alrashdi, Ibrahim Sallam, Karam M. Hameed, Ibrahim A. Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka Saudi Arabia Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates Univ Canberra Fac Sci & Technol Sch IT & Syst Canberra 2601 Australia Norwegian Univ Sci & Technol NTNU Dept ICT & Nat Sci N-7491 Alesund Norway

In recent years, unmanned aerial vehicles (UAVs) have been broadly employed as a data collection platform to assist in efficiently collecting data from IoT devices. However, the deployment optimization of UAVs has been challenged due to the need to minimize the energy consumption of UAVs and IoT devices. Several algorithms have been recently proposed for tackling this challenge, but they still have room for improvement due to their slow convergence speed and memory-wasting problems. Therefore, in this study, a new energy-aware approach has been proposed for accurately optimizing the entire deployment of UAVs, which could minimize the total energy consumption. This approach is based on presenting a new encoding mechanism, namely an optimized population size mechanism, for representing both location and number of stop points in an effective manner. In this mechanism, similar to some studies in the literature, the whole population is responsible for the entire deployment, and each individual is responsible for a stop point in this deployment. However, this mechanism presents a novel way to optimize the number of stop points based on adding an auxiliary variable to each stop point to determine whether it will be removed, inserted, or replaced in the newly generated deployment. This variable will be optimized by the optimization techniques during the optimization process to search for the optimal choice for each stop point that could achieve a better deployment. Two well-known optimization techniques, known as differential evolution (DE) and gradient-based optimizer (GBO), are adapted using this mechanism to present new variants, namely DEoPS and GBoPS, for accurately tackling the deployment optimization problem. Two energy consumption formulations are used in our work to investigate the performance of DEoPS and GBoPS. Several experiments have been conducted to compare the performance of both DEoPS and GBoPS with several algorithms on eleven instances. The experimental f

关键词： UAV Internet of Things Differential evolution gradient-based optimizer Deployment optimization Encoding Optimized population size

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gradient-based optimization with ranking mechanisms for parameter identification of photovoltaic systems

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ENERGY REPORTS 2021年 7卷 3979-3997页

作者： Ahmadianfar, Iman Gong, Wenyin Heidari, Ali Asghar Golilarz, Noorbakhsh Amiri Samadi-Koucheksaraee, Arvin Chen, Huiling Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan Iran China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China

Deriving optimal photovoltaic (PV) models' optimal parameters have tremendous significance in simulating, evaluating, and controlling the photovoltaic systems. Determining unknown parameters of these PV models is a multimodal, nonlinear, and complex optimization problem. Hence, developing a robust optimization model to achieve optimal parameters of the PV models effectively is essential. This paper proposes an enhanced metaphor-free gradient-based optimizer (EGBO) for extracting PV parameters quickly, precisely, and reliably. In the EGBO, a rank-based mechanism is employed to update its parameters efficiently. Also, the logistic map (LC) is implemented to better use the local escaping operator (LEO) in the original GBO algorithm. The proposed EGBO optimally identifies various parameters in the PV model, such as single diodes, double diodes, and PV modules. The relevant results indicate that compared with most advanced optimization methods, the EGBO algorithm is competitive in reliability, accuracy, and convergence speed. Moreover, the relevant results from the experimental data drawn from the manufacturer's datasheet demonstrate that the developed approach can offer highly accurate solutions at various irradiances and temperatures. Consequently, the achieved results confirm that the novel approach can be presented as a utility tool for deriving optimal PV models' optimal parameters, and it can be helpful in modeling PV systems. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Photovoltaic models Parameter identification gradient-based optimizer Optimization Swarm intelligence

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Noise reduction and feature enhancement of hob vibration signal based on parameter adaptive VMD and autocorrelation analysis

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MEASUREMENT SCIENCE AND TECHNOLOGY 2022年第12期33卷 125116-125116页

作者： Yuan, Yanfei Yan, Ping Zhou, Han Huang, Qin Wu, Dayuan Zhu, Junhan Ni, Zhongming Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Instrumentat Technol & Econ Inst Beijing 100055 Peoples R China Landai Technol Grp Co Ltd Chongqing 402760 Peoples R China

The vibration signal of the hobbing machine is susceptible to changes in the frequency domain distribution owing to the influence of the machine's inherent vibration and random pulses, which affects the condition monitoring and wear prediction of the hobbing machine. Variational mode decomposition (VMD) can compensate for the mode mixing problem of ensemble empirical mode decomposition method owing to its inherent equivalent filtering property. However, the decomposition performance of VMD depends heavily on two hyperparameters that need to be set in advance, i.e. the number of bandwidth-limited intrinsic mode functions (BLIMFs) K and the penalty factor alpha. Thus, a hybrid signal denoising and feature enhancement method based on parameter adaptive variational mode decomposition (PAVMD) and autocorrelation analysis is proposed in this study. First, a gradient-based optimizer is introduced to optimally select the decomposition parameter of VMD, and then a series of BLIMFs are obtained via VMD. Further, an evaluation criterion called enhanced periodic modulation intensity based on autocorrelation analysis is built to quantify the noise-related degree of each BLIMF. Finally, the denoised signal is obtained based on the proposed reconstruction strategy. The comparison with other methods in both the simulation and the actual signal analysis reveals that the proposed method has a better performance in terms of eliminating the high-frequency noise, reserving higher effective frequencies, and the evaluation indicator.

关键词： hob parameter adaptive variational mode decomposition gradient-based optimizer autocorrelation analysis

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Modified gradient-based Algorithm for Distributed Generation and Capacitors Integration in Radial Distribution Networks

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IEEE ACCESS 2023年 11卷 120899-120917页

作者： El-Rifaie, Ali M. Shaheen, Abdullah M. Tolba, Mohamed A. Smaili, Idris H. Moustafa, Ghareeb Ginidi, Ahmed R. Elshahed, Mostafa A. Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait Suez Univ Fac Engn Elect Engn Dept Suez 43533 Egypt Egyptian Atom Energy Author Nucl Res Ctr Reactors Dept Cairo 11787 Egypt Jazan Univ Elect Engn Dept Jazan 45142 Saudi Arabia Engn & Informat Technol Coll Elect Engn Dept Buraydah 51418 Saudi Arabia Buraydah Private Coll Elect Engn Dept Buraydah 51418 Saudi Arabia Cairo Univ Fac Engn Elect Power Engn Dept Cairo 12613 Egypt

With the use of cutting-edge demand management at the system or home level and powerful network reconfiguration tools, smart grids are expected to introduce advanced hardware and software resources to strengthen the operation of power systems. This article describes a Modified gradient-based Optimization (MGBO) algorithm for Distributed Generation (DG) and capacitors integration in distribution feeders. To increase the variety of the produced searching individuals, the suggested MGBO combines the basic gradient Searching Method (GSM) and Local Escape Mechanism (LEM) with a binomial crossover strategy. This combined cross-over strategy upgrades the forthcoming searching individuals to be more random. The LEM assists in evading local optima, whereas the GSM guides the searching scan to promising regions and facilitates its convergence to the optimum solution. The suggested MGBO method is designed and implemented to improve the performance of radial distribution networks by reducing technical power losses while taking into account the peak loading. Its relevance is tested on a practical radial 59-bus Cairo distribution feeder in Egypt and a large-scale radial 135-bus distribution feeder. The proposed MGBO is compared with the original GBO, Manta ray foraging optimization (MRFO) and honey badger algorithm (HBA). The whole comparison of the suggested MGBO with the original GBO and the newly developed optimization algorithms demonstrates that the suggested MGBO derives the best performance in all of the cases studied. For the practical radial 59-bus Cairo distribution feeder in Egypt, the proposed MGBO shows great improvement of 18.40%, 20.17%, and 2.29% in robustness indicator of the standard deviation compared with GBO, MRFO, and HBA, respectively. For the large-scale radial 135-bus distribution feeder, the proposed MGBO shows great improvement of 46.92%, 62.94%, and 67.87% in robustness indicator of the standard deviation compared with GBO, MRFO, and HBA, respectively.

关键词： gradient-based optimizer distributed generation capacitors distribution systems power losses minimization

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Robust topology optimization of skeletal structures with imperfect structural members

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2018年第6期58卷 2533-2544页

作者： Ahmadi, Babak Jalalpour, Mehdi Asadpoure, Alireza Tootkaboni, Mazdak Univ Massachusetts Dept Civil & Environm Engn N Dartmouth MA 02747 USA Cleveland State Univ Dept Civil & Environm Engn Cleveland OH 44115 USA

A topology optimization framework is proposed for robust design of skeletal structures with stochastically imperfect structural members. Imperfections are modeled as uncertain members' out-of-straightness using curved frame elements in the form of predefined functions with random magnitudes throughout the structure. The stochastic perturbation method is used for propagating the imperfection uncertainty up to the structural response level, and the expected value of performance measure or constraint is used to form the stochastic topology optimization problem. Sensitivities are derived explicitly using the adjoint method and are used in conjunction with an efficient gradient-based optimizer in search for robust optimal topologies. Topological designs for three representative examples are investigated with the proposed algorithm and the resulting topologies are compared with the deterministic designs. It is observed that the new designs primarily feature load path diversification, which is pronounced with increasing level of uncertainty, and occasionally member thickening to mitigate the impact of the uncertainty in members' out-of-straightness on structural performance.

关键词： Topology optimization gradient-based optimizer Adjoint method Structural imperfections Stochastic perturbation Curved beam element

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A hybrid capuchin search algorithm with gradient search algorithm for economic dispatch problem

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SOFT COMPUTING 2023年第22期27卷 16809-16841页

作者： Braik, Malik Awadallah, Mohammed A. A. Al-Betar, Mohammed Azmi Hammouri, Abdelaziz I. I. Al Balqa Appl Univ Dept Comp Sci Salt Jordan Al Aqsa Univ Dept Comp Sci Gaza Palestine Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Hosn Univ Coll Dept Informat Technol Irbid Jordan

This paper presents an effective approach for solving economic load dispatch problems contemplating the scheduling a set of thermal generating units to produce a specific power at low consumption costs. These problems can be thought of as nonlinear, non-convex, and highly constrained optimization problems with a large number of local minima. To cope with the above issues in solving such problems, a new meta-heuristic named capuchin search algorithm was adopted. To boost the search performance of this algorithm as well as to mitigate its early convergence and regression to the local optimum, it was hybridized with another algorithm and improved using several positive amendments. First, a memory element was added to this algorithm to ameliorate its position and velocity update mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive parametric functions were used to manage the exploration and exploitation features of this algorithm and balance them appropriately. Finally, the hybridization was made using the gradient-based optimizer to strengthen the intensification ability of this algorithm and balance its searching ability to fulfill sensible search performance. The proficiency of the proposed algorithm was divulged by assessing it on computationally difficult economic load dispatch problems under 6 different tests with a generator of 3, 13, 40, 80, and 140 units, each with different constraints and load conditions. The proposed algorithm provided the best performance among many other competitors. Its superiority and practicality were revealed by obtaining optimal solutions for large-scale test cases such as 40-unit and 140-unit test systems.

关键词： Economic load dispatch Capuchin search algorithm gradient-based optimizer Memory concept Optimization

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