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Inflow Prediction of Centralized Reservoir for the Operation of Pump Station in Urban Drainage Systems Using Improved Multilayer Perceptron Using Existing Optimizers Combined with metaheuristic optimization algorithms

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WATER 2023年第8期15卷 1543页

作者： Lee, Eui Hoon Chungbuk Natl Univ Sch Civil Engn Cheongju 28644 South Korea

Owing to the recent increase in abnormal climate, various structural measures including structural and non-structural approaches have been proposed for the prevention of potential water disasters. As a non-structural measure, fast and safe drainage is an essential preemptive operation of a drainage facility, including a centralized reservoir (CRs). To achieve such a preemptive operation, it is necessary to predict the inflow of the drainage facilities. Among the drainage facilities, CRs are located downstream of the drainage area, and their pump stations are operated according to the CR water level. The water level of a CR depends on the inflow, as does the preemptive operation of its pump station. In this study, as a nonstructural measure, the inflow prediction for the CR operation in an urban drainage system was proposed. For predicting the inflow of a CR, a new multilayer perceptron (MLP) using existing optimizers combined with a self-adaptive metaheuristic optimization algorithm, such as an improved harmony search, was proposed. Compared with the adaptive moment, which yields the best results among other existing optimizers, an MLP using an existing optimizer combined with an improved harmony search improves the mean square error and mean absolute error by 0.1767 and 0.0349, respectively.

关键词： centralized reservoir urban drainage system multilayer perceptron metaheuristic optimization algorithm improved harmony search

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Developed comparative analysis of metaheuristic optimization algorithms for optimal active control of structures

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ENGINEERING WITH COMPUTERS 2020年第4期36卷 1539-1558页

作者： Katebi, Javad Shoaei-parchin, Mona Shariati, Mahdi Trung, Nguyen Thoi Khorami, Majid Univ Tabriz Fac Civil Engn Tabriz *** Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Ton Duc Thang Univ Inst Computat Sci Div Computat Math & Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Univ UTE Fac Arquitectura & Urbanismo Calle Rumipamba S-N & Bourgeois Quito Ecuador

A developed comparative analysis of metaheuristic optimization algorithms has been used for optimal active control of structures. The linear quadratic regulator (LQR) has ignored the external excitation in solving the Riccati equation with no sufficient optimal results. To enhance the efficiency of LQR and overcome the non-optimality problem, six intelligent optimization methods including BAT, BEE, differential evolution, firefly, harmony search and imperialist competitive algorithm have been discretely added to wavelet-based LQR to seek the attained optimum feedback gains. The proposed approach has not required the solution of Riccati equation enabling the excitation effect in controlling process. Employing this advantage by each of six mentioned algorithms to three-story and eight-story structures under different earthquakes led to define (1) the best solution, (2) convergence rate and (3) computational effort of all methods. The purpose of this research is to study the aforementioned methods besides the superiority of ICA in finding the optimal responses for active control problem. Numerical simulations have confirmed that the proposed controller is enabling to significantly reduce the structural responses using less control energy compared to LQR.

关键词： Active control metaheuristic optimization algorithm Linear quadratic regulator (LQR) Discrete wavelet transform (DWT)

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Techno-economic evaluation and sensitivity analysis of renewable energy based designing of plug-in electric vehicle load considering load following strategy

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APPLIED ENERGY 2025年第PartC期377卷

作者： Bilal, Mohd Ahmad, Fareed Mohammad, Arshad Rizwan, Mohammad SND Coll Engn & Res Ctr Dept Elect Engn Nasik 423401 India Aligarh Muslim Univ Dept Elect Engn Aligarh 202002 India Delhi Technol Univ Dept Elect Engn Delhi 110042 India

This work explores the deployment of renewable based energy systems considering solar panels, wind turbines and battery energy storage for the charging of Plug-in Electric Vehicles (PEVs) in the Noida region. The main concern of this article is to establish the optimal sizing of system components in order to reduce energy costs and the possibility of power outages. To accomplish these goals, this research employs a unique metaheuristic-based optimization strategy known as the Giza Pyramid Construction technique (GPCA). The superiority of the solution provided by the GPCA is proven by comparing the results obtained using Grey Wolf optimization (GWO), Flower Pollination algorithm (FPA), Salp Swarm algorithm (SSA) and Moth Flame optimization (MFO). The algorithms used in the study are simulated 50 times with various values of loss of power supply probability (LPSP) such as 0 %, 1 %, 3 %, and 5 %. The simulation results show that the GPCA achieves the desired objectives with high accuracy and resilience. The study also examined how varying grid tariffs influenced the levelized cost of energy. The findings revealed that, when compared to other options, the solar/wind/battery combination had a significantly lower levelized cost of energy and overall net present cost. The total net present cost estimated by GPCA is lower by 7.9 %, 14.1 %, 17.9 %, and 24.5 % compared to the costs calculated using GWO, MFO, SSA, and FPA, respectively. Similarly, the GPCA provides optimized value of LCOE (0.3697 $/kWh) which is 3.1 %, 7.3 %, 8.8 % and 11.5 % less than GWO, MFO, SSA and FPA respectively. The outcomes of this research will provide valuable insights for researchers aiming to determine the most effective strategy for powering PEV charging through a multi-energy system approach. This information can be beneficial for other cities seeking to establish a similar strategy. The proposed system holds the potential to reduce dependence on overloaded grids, especially in developing citie

关键词： metaheuristic optimization algorithm Renewable energy sources Energy storage system plug-in electric vehicles Solar photovoltaic Wind turbine

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Hierarchical Compensation of Robot Positioning Error: Addressing Geometric and Nongeometric Influences

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Xu, Shuo Jia, Xiaohui Liu, Jinyue Li, Tiejun Hebei Univ Technol Sch Mech Engn Tianjin 300401 Peoples R China Hebei Univ Sci & Technol Sch Mech Engn Shijiazhuang 050018 Peoples R China

Aiming at the problem that the absolute positioning accuracy of the robot end-effector decreases due to geometric errors, such as those from manufacturing and assembly, and nongeometric errors, such as joint flexibility and gear clearance in industry, a hierarchical compensation method of robot positioning error is proposed. First, an improved crayfish optimization algorithm (ICOA) and a multiobjective ICOA (MOICOA) are proposed. Aiming at the model condition number and position distribution, the identification points are optimized by a multiobjective approach. Moreover, the geometric error of the robot is compensated with the minimum positioning error of the robot end-effector as the goal. Second, based on data-driven, the Gaussian process regression (GPR) model is established, and its hyperparameter is optimized by ICOA so as to regress and compensate the nongeometric errors of the robot. Finally, the experiment of hierarchical compensation of positioning error is conducted using a universal six-degree-of-freedom (6-DOF) serial robot. The experimental results show that the maximum error, mean absolute error, and root-mean-square error (RMSE) of the position error are reduced by 95.43%, 92.39%, and 95.01%, respectively, after geometric error and nongeometric error compensation, which verifies the correctness and effectiveness of this method. The research results of this article provide a theoretical basis for the robot's high-precision operation.

关键词： Robots Robot kinematics optimization End effectors Kinematics Accuracy metaheuristics Error compensation Parameter estimation Heuristic algorithms Absolute positioning accuracy data-driven industrial robots metaheuristic optimization algorithm parameter identification

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Experimental and comparative study on optimal Active and Reactive Energy Management in microgrid: Moroccan VS Time of Use Tariff

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2025年 212卷

作者： Gheouany, Saad Ouadi, Hamid El Bakali, Saida Mohammed V Univ Rabat Natl Higher Sch Arts & Crafts ERERA Rabat Morocco

This research introduces an innovative Active and Reactive Energy Management System (AR-EMS) of a Smart Microgrid (SMG) supplying various types of consumers and using different pricing schemes. The research explores the electricity pricing and the regulations in force impacts on energy flow distribution, bill and Storage System (SS) lifespan. It compares two pricing tariffs: the Moroccan tiered pricing system (TTS) and the European Time-of-Use (TOU) tariff. The proposed AR-EMS calculates optimal active and reactive power setpoints for each energy source over 30 days, using load consumption and photovoltaic (PV) power forecasts. The optimization problem is solved using particle swarm optimization (PSO), aiming to simultaneously reduce the energy cost, SS degradation, Peak-to-Average Ratio (PAR), and environmental impact. Experimental validation is conducted with real data from a lab in Rabat, Morocco. The research critically analyzes and compares the performance of the proposed AR-EMS with an existing industrial Active Energy Management System (A-EMS). The study also highlights the potential earnings by enabling excess energy to be injected into the Moroccan grid. The robustness and scalability of the proposed AR-EMS are validated using industrial consumer data. Comparative analysis reveals several advantages of the AR-EMS with respect to the considered industrial A-EMS, including a 6% reduction in energy costs, a 73% decrease in PAR, and a 17% reduction in carbon emissions while improving the power factor by 31%. Finally, when applied to the industrial consumer data, the proposed solution demonstrates robustness and advantages results.

关键词： Building automation Microgrids Active and reactive power dispatch strategy metaheuristic optimization algorithm Moroccan Tiered Electricity Tariff Time of Use Tariff Battery storage

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Prediction of chloride concentration in concrete under multi-salt environment: optimization of integrated algorithm based on MSCPO and interpretability analysis

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MATERIALS & DESIGN 2025年 251卷

作者： Luo, Daming Du, Kanglei Niu, Ditao Xian Univ Architecture & Technol Sch Civil Engn Xian 710055 Peoples R China Xian Univ Architecture & Technol State Key Lab Green Bldg Xian 710055 Peoples R China

The accurate prediction of chloride concentration is vital for assessing reinforced concrete structure durability. However, diverse erosion media in engineering environments with varying ion concentrations present challenges for traditional prediction methods. This study conducted accelerated experiments to create a concrete chloride ion dataset in a multi-salt environment, with adjustments for abnormal data. The Crested Porcupine Optimizer (CPO) algorithm was enhanced with adaptive techniques, and the refined strategy's effectiveness was verified through test function analysis. The Improved Mixture Self-Adaptation Crested Porcupine Optimizer (MSCPO) optimized hyperparameters for XGBoost, LightGBM, and Catboost models separately. The fitting, accuracy, and stability of each model in predicting concrete chloride concentration were quantitatively assessed. SHAP was used to explain the best-performing model, and its reliability was supported by microscopic observation results and literature. Results show that identifying and handling outliers enhance model performance. The proposed MSCPO excelled in hyperparameter search, with optimized ensemble models maintaining error within a reasonable range. XGBoost had the best performance, completing hyperparameter search in 45.52 s and achieving an R2 of 92.86%. SHAP results aligned closely with experiments and supported existing literature conclusions.

关键词： Multiple ions Chloride concentration metaheuristic optimization algorithm Interpretability analysis Intelligent prediction

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A Generalized Framework for Multi-objective-based Constraint Handling Technique

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2024年第10期22卷 3240-3252页

作者： Dam, Tien Minh Truong, Long Viet Viettel High Technol Ind Corp Subsidiary Viettel Grp Hanoi 10000 Vietnam

This paper introduces a novel approach to handling constraints in multi-objective optimization problems by treating constraints as objectives. Our study consists of three main contributions. First, we present a generalized framework for multi-objective-based constraint handling techniques, which integrates multi-objective-based methods with the constraint dominance principle. This framework systematically incorporates various aspects of constraint violations into the objective function, enhancing optimization strategies. Second, we propose the constraint violation ratio, a metric that uses a constraint weight vector to quantify the severity of violations. Third, we develop the constraint diversity factor, an adaptive version of the constraint weight vector, which adapts as the frequency of constraint violations changes. Extensive experimental evaluations validate the effectiveness of our methods, demonstrating their applicability across a wide range of constrained optimization scenarios.

关键词： Constrained multi-objective evolutionary algorithm constrained multi-objective optimization problem constraint as objectives constraint handling techniques generalized framework for constraint handling metaheuristic optimization algorithm

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An evolutionary hybrid method based on particle swarm optimization algorithm and extreme gradient boosting for short-term streamflow forecasting

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ACTA GEOPHYSICA 2024年第5期72卷 3661-3681页

作者： Kilinc, Huseyin Cagan Haznedar, Bulent Ozkan, Furkan Katipoglu, Okan Mert Istanbul Aydin Univ Dept Civil Engn Istanbul Turkiye Gaziantep Univ Dept Comp Engn Gaziantep Turkiye Cukurova Univ Dept Comp Engn Adana Turkiye Erzincan Binali Yildirim Univ Dept Civil Engn Erzincan Turkiye

Streamflow estimation is necessary to develop sustainable water management strategies that balance the needs of various water users while protecting the ecological health of the watershed. This study presents a comprehensive analysis of river flow data in Turkey's Meric basin using long-term daily data spanning the years 2001-2011. Within the scope of forecasting of daily river flows, 80% of dataset was used for training stage and the 20% of dataset was used for testing. The performance of four different models, including Extreme Gradient Boosting (XGBoost) benchmark model, Linear Regression, Long Short-Term Memory Network (LSTM), and proposed Particle Swarm optimization (PSO)-XGBoost hybrid model, was evaluated using several statistical evaluation criteria such as RMSE, MAPE, MAE, SD, NSE, KGE, and model success was assessed based on determination coefficient (R2) values. The forecasting results of the models were evaluated with statistical metrics. The proposed PSO-XGBoost model outperformed all models with R2 values of 0.7460 at the Babaeski FMS station, 0.9582 at the Meric FMS station, and 0.8116 at the Hayrabolu FMS station, respectively. It has been found that when the parameters of the XGBoost model are optimized with the meta-heuristic PSO algorithm, the daily flow prediction accuracy is significantly increased in the Meric River basin, which has a pluviozonal River regime. The results demonstrate that the proposed hybrid model produces precise and reliable estimations for daily river flow analysis in the Meric basin. The findings derived from the proposed model demonstrate its promising potential for water resource management and decision-making in the region.

关键词： Proposed model Prediction Machine learning metaheuristic optimization algorithm Streamflow

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A hybrid encryption model for the hyperspectral images: application to hyperspectral medical images

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第4期83卷 11717-11743页

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet St Longowal Inst Engn & Technol Dept Comp Sci & Engn Longowal 148106 Punjab India St Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Longowal 148106 Punjab India

Hyperspectral images collect information across the electromagnetic spectrum and are widely used to recognize signals, identify materials, and find objects. However, the information in a hyperspectral image may be sensitive and must be protected. Therefore, this paper proposes a new information confidentiality model for hyperspectral images. In this model, Self Adaptive Bald Eagle Search (SABES) optimization algorithm is proposed to select the initial and control parameters of the chaos maps to improve the encryption process. A multi-level chaotic system is implemented to enhance the security of the hyperspectral images by increasing the randomness. A circular shift is utilized to secure the proposed model from differential attack. The proposed encryption model is authenticated with various security analyses. The experimental results show that the proposed cryptographic model is secure from different attacks (statistical, differential, noise, and cropping). The performance of the proposed model is validated by comparing the results of the proposed model with the state-of-the-art methods. The results prove that the proposed model is more secure and requires less computational time for image encryption and decryption in comparison to the existing methods. In addition, the proposed encryption model is applied to secure the hyperspectral medical images, demonstrating its utility in the medical field.

关键词： Chaotic maps Decryption Encryption Hyperspectral images metaheuristic optimization algorithm

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Effect of fitness function on localization performance in range-free localization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第4期83卷 9761-9784页

作者： Han, Fengrong Abdelaziz, Izzeldin Ibrahim Mohamed Ghazali, Kamarul Hawari Zhao, Yue Baoji Univ Arts & Sci Sch Comp Baoji 721000 Peoples R China Univ Malaysia Pahang Coll Engn Kuantan 26300 Pahang Malaysia Univ Malaysia Pahang Fac Elect & Elect Engn Pekan 26600 Pahang Malaysia Baoji Univ Arts & Sci Coll Geog & Environm Baoji 721016 Peoples R China

The problem of solving the nonlinear equations in the range-free localization algorithm has been transformed into an optimal solution problem. Meta-heuristic optimization method has been widely adopted to tackle above issues. How to choose the best localization fitness function for a specific target is a key factor in determining whether the localization algorithm is accurate or not. However, so far there is no literature to investigate the effect of fitness function on rang-free localization algorithm. Firstly, this study comprehensively reviews and classifies the frequently-used localization fitness function in range-free localization scheme. Next, multiple experiments are carried out for each typical localization fitness function. The experimental results are analyzed in terms of accuracy and stability. Besides, the advantage and disadvantage of each localization fitness function are also given. Finally, an advanced localization fitness function is proposed based on the above experimental results, which will provide a guide and reference for selection and improvement of the fitness function in range-free localization algorithm.

关键词： Wireless sensor network (WSNs) DV-Hop Localization algorithm metaheuristic optimization algorithm Fitness function Particle Swarm optimization (PSO)

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