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A Combination of metaheuristic optimization algorithms and Machine Learning Methods Improves the Prediction of Groundwater Level

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WATER 2022年第5期14卷 751页

作者： Kayhomayoon, Zahra Babaeian, Faezeh Milan, Sami Ghordoyee Azar, Naser Arya Berndtsson, Ronny Payame Noor Univ Dept Geol Tehran 193954697 Iran Islamic Azad Univ Tehran Sci & Res Branch Dept Water Sci & Engn Tehran *** Iran Univ Tehran Dept Irrigat & Drainage Engn Aburaihan Campus Tehran *** Iran Univ Tabriz Dept Water Engn Fac Agr Tabriz *** Iran Lund Univ Div Water Resources Engn SE-22100 Lund Sweden Lund Univ Ctr Adv Middle Eastern Studies SE-22100 Lund Sweden

Groundwater is a crucial source of water supply in drought conditions, and an auxiliary water source in wet seasons. Due to its increasing importance in view of climate change, predicting groundwater level (GWL) needs to be improved to enhance management. We used adaptive neuro-fuzzy inference systems (ANFIS) to predict the GWL of the Urmia aquifer in northwestern Iran under various input scenarios using precipitation, temperature, groundwater withdrawal, GWL during the previous month, and river flow. In total, 11 input patterns from various combinations of variables were developed. About 70% of the data were used to train the models, while the rest were used for validation. In a second step, several metaheuristic algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR), and differential evolution (DE) were used to improve the model and, consequently, prediction performance. The results showed that (i) RMSE, MAPE, and NSE of 0.51 m, 0.00037 m, and 0.86, respectively, were obtained for the ANFIS model using all input variables, indicating a rather poor performance, (ii) metaheuristic algorithms were able to optimize the parameters of the ANFIS model in predicting GWL, (iii) the input pattern that included all input variables resulted in the most appropriate performance with RMSE, MAPE, and NSE of 0.28 m, 0.00019 m, and 0.97, respectively, using the ANIFS-ACOR hybrid model, (iv) results of Taylor's diagram (CC = 0.98, STD = 0.2, and RMSD = 0.30), as well as the scatterplot (R-2 = 0.97), showed that best prediction was achieved by ANFIS-ACOR, and (v) temperature and evaporation exerted stronger influence on GWL prediction than groundwater withdrawal and precipitation. The findings of this study reveal that metaheuristic algorithms can significantly improve the performance of the ANFIS model in predicting GWL.

关键词： ANFIS groundwater machine learning metaheuristic optimization algorithms time series Urmia aquifer

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Performance analysis of metaheuristic optimization algorithms in estimating the parameters of several wind speed distributions

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APPLIED ENERGY 2020年 268卷 114952-114952页

作者： Guedes, Kevin S. de Andrade, Carla F. Rocha, Paulo A. C. Mangueira, Rivanilso dos S. de Moura, Elineudo P. Univ Fed Ceara Technol Ctr Mech Engn Dept BR-60020181 Fortaleza Ceara Brazil Univ Fed Ceara Technol Ctr Dept Met & Mat Engn BR-60020181 Fortaleza Ceara Brazil

For a better use of wind energy, the accurate selection of the wind speed distributions that best represents the regarding wind regime's characteristics is essential. The Weibull distribution is the most common, but this model is not always the most suitable. Therefore, in order to obtain more reliable information, the evaluation of different distributions becomes necessary. Another crucial step is the estimation of the parameters that govern these distributions because the accuracy of these estimates directly affects the energy generation calculations. In the last few years, different optimization methods have been used for this purpose. However, the applications of these methods are focused on conventional two-parameter distributions, such as Weibull and Lognormal. Futhermore, different authors report that there is a lack of studies that use optimization methods for this purpose. In this paper, four metaheuristic optimization algorithms (MOA)-namely, Migrating Birds optimization (MBO), Imperialist Competitive Algorithm (ICA), Harmony Search (HS) and Cuckoo Search (CS)-are used to fit 11 distributions in two Brazillian regions. Thus, this work expands the application of the MOA to beyond the conventional distributions and applies, for the first time, the MBO and ICA in estimating the parameters of wind speed distributions, thereby introducing new ways to optimize the use of wind resources. The fits obtained by the MOA were compared with those obtained by the method Maximum Likelihood Estimation (MLE). Gamma Generalized and Extended Generalized Lindley distributions presented the best fits, and the MOA outperformed the MLE because the global score values obtained were smaller.

关键词： Wind energy Wind speed modeling Optimal parameters estimation Multi-criteria statistical analysis metaheuristic optimization algorithms Non-conventional wind speed distributions

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Performance Comparison of metaheuristic optimization algorithms Using Water Distribution System Design Benchmarks 4th

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4th International Conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Lee, Ho Min Jung, Donghwi Sadollah, Ali Lee, Eui Hoon Kim, Joong Hoon Korea Univ Res Ctr Disaster Prevent Sci & Technol Seoul South Korea Sharif Univ Technol Dept Mech Engn Tehran Iran Korea Univ Sch Civil Environm & Architectural Engn Seoul South Korea

ISBN: (纸本)9789811307614;9789811307607

Various metaheuristic optimization algorithms are being developed and applied to find optimal solutions of real-world problems. Engineering benchmark problems have been often used for the performance comparison among metaheuristic algorithms, and water distribution system (WDS) design problem is one of the widely used benchmarks. However, only few traditional WDS design problems have been considered in the research community. Thus, it is very challenging to identify an algorithm's better performance over other algorithms with such limited set of traditional benchmark problems of unknown characteristics. This study proposes an approach to generate WDS design benchmarks by changing five problem characteristic factors which are used to compare the performance of metaheuristic algorithms. Obtained optimization results show that WDS design benchmark problems generated with specific characteristic under control help identify the strength and weakness of reported algorithms. Finally, guidelines on the selection of a proper algorithm for WDS design problems are derived.

关键词： metaheuristic optimization algorithms Performance measurement Water distribution systems

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Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2017年第Mar.期69卷 1199-1217页

作者： Jiang, Haiyan Wang, Jianzhou Wu, Jie Geng, Wei Nankai Univ Inst Stat Tianjin 300071 Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Northwest Univ Nationalities Math & Comp Inst Lanzhou 730030 Peoples R China

Recently, with energy crises and environmental problems becoming increasingly obvious, the utilization of wind power has become a big concern. Meanwhile, the inconsistent relationship between China's economy and wind energy potential distribution has caused inevitable difficulties in transportation of wind power and even in grid integration. Therefore, the establishment of electrical power system integrated with local-used low-speed wind power has got considerable attention. Weibull, Rayleigh, Gamma and Lognormal probability distributions are evaluated. Then three numerical methods (NMs) - method of moment (MM), maximum likelihood estimation (MLE), and least squares method (LSM), are applied to get parameter estimation in the these distributions. Additionally, another three comparison metaheuristic optimization algorithms (MOAs), including bat algorithm (BA), cuckoo search algorithm (CS) and particle swarm optimization (PSO) are employed as comparison methods to tune the optimal parameters. Experimental results conclude that in this case MOAs perform better than NMs. Moreover, BA-Weibull, CS-Weibull, and PSO-Weibull with only a slight difference outperform all of the other distributions. Specifically, BA-Weibull and PSO-Weibull are only slightly superior to CS-Weibull. The average wind power density, the effective wind power density, the available factor and the capacity factor of wind turbine are considered as key determinant factors in assessing the low-speed wind energy potential, which are directly influenced by the parameters in Weibull model. Moreover, the wind potential assessment in the low-speed wind areas can provide an essential technique support for further investment and development, even for further wind farm construction and economy evaluation. Consequently, accurate parameter estimation is of great importance in low speed wind energy resource assessment.

关键词： metaheuristic optimization algorithms BA CS PSO Low-speed wind assessment

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The design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms: a review of the literature

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ARTIFICIAL INTELLIGENCE REVIEW 2015年第2期44卷 265-287页

作者： Kockanat, Serdar Karaboga, Nurhan Cumhuriyet Univ Sivas Vocat Sch Elect Commun Technol TR-58140 Sivas Turkey Erciyes Univ Dept Elect Engn Fac Engn TR-38039 Kayseri Turkey

Recently, the design of the two-dimensional digital filter has become a subject of interest in the field of two-dimensional signal processing. The two-dimensional digital filter has been applied in many important areas such as image processing, television systems and seismic signal processing. In digital filter design, there are several indispensable aims such as stability, reduced computational complexity and computational time. Thus, researchers and practitioners have investigated various advanced methods based on metaheuristic optimization algorithms for the design of the two-dimensional digital filter. metaheuristic optimization algorithms have been applied to solve different complicated problems in various fields and they have also been successfully used in digital filter design. This paper presents a review of the design approaches of two-dimensional digital filters based on metaheuristic optimization algorithms such as the genetic algorithm, differential evolution and particle swarm optimization. By comparing the proposed design approaches based on metaheuristic optimization algorithms, it is observed that the genetic algorithm is the most preferred algorithm and emerging novel algorithms using metaheuristic optimization algorithms have better performance in terms of computational complexity and computational time. It is hoped that this review will be helpful for researchers and practitioners studying the design of two-dimensional digital filters.

关键词： Two-dimensional digital filter metaheuristic optimization algorithms Filter design optimization Swarm intelligence Evolutionary algorithm

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Recent metaheuristic algorithms for solving some civil engineering optimization problems

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SCIENTIFIC REPORTS 2025年第1期15卷 1-25页

作者： Houssein, Essam H. Gafar, Mohamed Hossam Abdel Fawzy, Naglaa Sayed, Ahmed Y. Minia Univ Fac Comp & Informat Al Minya Egypt Minia Natl Univ Al Minya Egypt Helwan Univ Fac Engn Mataria Phys & Engn Math Dept Cairo Egypt

In this study, a novel hybrid metaheuristic algorithm, termed (BES-GO), is proposed for solving benchmark structural design optimization problems, including welded beam design, three-bar truss system optimization, minimizing vertical deflection in an I-beam, optimizing the cost of tubular columns, and minimizing the weight of cantilever beams. The performance of the proposed BES-GO algorithm was compared with ten state-of-the-art metaheuristic algorithms: Bald Eagle Search (BES), Growth Optimizer (GO), Ant Lion Optimizer, Tuna Swarm optimization, Tunicate Swarm Algorithm, Harris Hawk optimization, Artificial Gorilla Troops Optimizer, Dingo Optimizer, Particle Swarm optimization, and Grey Wolf Optimizer. The hybrid algorithm leverages the strengths of both BES and GO techniques to enhance search capabilities and convergence rates. The evaluation, based on the CEC'20 test suite and the selected structural design problems, shows that BES-GO consistently outperformed the other algorithms in terms of convergence speed and achieving optimal solutions, making it a robust and effective tool for structural optimization.

关键词： metaheuristic optimization algorithms Blade eagle search Welded beam design Civil engineering

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ANFIS-MOH: Systematic exploration of hybrid ANFIS frameworks via metaheuristic optimization hybridization with evolutionary and swarm-based algorithms

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Wang, Haoyu Chen, Bin Sun, Hangling Li, Anji Zhou, Chenyu Univ Shanghai Sci & Technol Sch Mech Engn Shanghai Peoples R China Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai Peoples R China Hengtu Imalligent Technol Shanghai Co Ltd Shanghai Peoples R China Abbott Labs Shanghai Co Ltd Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China Xinjiang Univ Sch Comp Sci & Technol Urumqi Peoples R China Tsinghua Univ Beijing Peoples R China

The adaptive neuro-fuzzy inference system (ANFIS) has shown promising performance in modeling nonlinear problems, leveraging the strengths of both neural networks and fuzzy inference systems. However, as the problem scale increases, the growing number of tunable parameters in ANFIS can make it challenging to optimize via traditional gradient-based methods alone. This study introduces ANFIS-MOH, a novel framework that synergistically integrates ANFIS with metaheuristic optimization algorithms to address these challenges. By leveraging the global search capabilities of metaheuristics such as ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA), ANFIS-MOH enhances the parameter tuning process of ANFIS models. We evaluate ANFIS-MOH on benchmark datasets including Boston Housing and Wine Quality, demonstrating significant improvements in prediction accuracy and generalization compared to traditional ANFIS and neural network approaches. The proposed framework achieves up to 20% reduction in Mean Squared Error and 15% increase in R2 2 scores, particularly excelling in handling high- dimensional, noisy data. This work contributes to the field of hybrid intelligent systems by introducing effective ways to combine the strengths of ANFIS with powerful metaheuristic optimization algorithms. The findings suggest that such hybrid approaches can be effective in tackling challenging nonlinear modeling problems. Our code is available at https://***/AmbitYuki/metaheuristic-Adaptive-ANFIS.

关键词： Adaptive neuro-fuzzy inference metaheuristic optimization algorithms Nonlinear modeling and regression Hybrid intelligent systems

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metaheuristic optimization for Sustainable Unrelated Parallel Machine Scheduling: A Concise Overview With a Proof-of-Concept Study

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IEEE ACCESS 2024年 12卷 3386-3416页

作者： Ezugwu, Absalom El-Shamir North West Univ Unit Data Sci & Comp ZA-2520 Potchefstroom South Africa

Sustainable development has emerged as a global priority, and industries are increasingly striving to align their operations with sustainable practices. Parallel machine scheduling (PMS) is a critical aspect of production planning that directly impacts resource utilization and operational efficiency. In this paper, we investigate the application of metaheuristic optimization algorithms to address the unrelated parallel machine scheduling problem (UPMSP) through the lens of sustainable development goals (SDGs). The primary objective of this study is to explore how metaheuristic optimization algorithms can contribute to achieving sustainable development goals in the context of UPMSP. We examine a range of metaheuristic algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and more, and assess their effectiveness in optimizing the scheduling problem at hand. The evaluation of the algorithms focuses on their ability to improve the optimization of job-to-machine assignments, enabling industries to efficiently minimize the overall makespan of scheduling tasks. This, in turn, leads to waste reduction and enhanced energy efficiency. To conduct a comprehensive analysis, we consider UPMSP instances that incorporate sustainability-related constraints and objectives. We assess the algorithms' performance in terms of solution quality, convergence speed, robustness, and scalability, while also examining their implications for sustainable resource allocation and environmental stewardship. The findings of this study provide insights into the efficacy of metaheuristic optimization algorithms for addressing UPMSP with a focus on sustainable development goals. By leveraging these algorithms, industries can optimize scheduling decisions to minimize waste and enhance energy efficiency. The practical implications of this research are valuable for decision-makers, production planners, and researchers seeking to achieve sustainable development goal

关键词： optimization metaheuristics Job shop scheduling Sustainable development Parallel machines Industries Resource management Sustainable development goals metaheuristic optimization algorithms unrelated parallel machine scheduling resource utilization energy consumption environmental impact

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metaheuristic optimization Algorithm-Based Enhancement of Photovoltaic Energy System Performance

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第11期48卷 14789-14810页

作者： Nasr, Rowan Abou-Zalam, Belal Nabil, Essam Menoufia Univ Fac Elect Engn Dept Ind Elect & Control Engn Menoufia Egypt

Photovoltaic panels (PVs) are solar panels that turn sunlight into electricity. Tracking the maximum power point (MPP) of PVs is especially important for economic issues. The most popular maximum power point tracking techniques are perturb and observation, hill climbing, constant voltage, parasitic capacitance, and incremental conductance (INC). However, these techniques give oscillated results about the MPP that causes low accuracy, especially in partial shading conditions. This paper is discussing the enhancement of photovoltaic energy system performance using several metaheuristic optimization algorithms. Using MATLAB SIMULINK, a comparative analysis of several algorithms for tracking MPP of PV systems under partially shadowed conditions was conducted. The metaheuristic optimization algorithms that are used in this paper are particle swarm optimization (PSO), cuckoo search algorithm (CSA), grey wolf optimization (GWO), and whale optimization algorithm (WOA). The results show that using WOA and GWO achieved the best efficiency in tracking MPP, whereas, using PSO and CSA achieved lower efficiency in tracking MPP. The MPP of the PV system was not tracked by INC under the partial shaded conditions.

关键词： Photovoltaic system MPPT Incremental conductance INC metaheuristic optimization algorithms Particle swarm optimization PSO Cuckoo search algorithm CSA Grey wolf optimization GWO Whale optimization algorithm WOA

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Boundary Strategy for optimization-based Structural Damage Detection Problem using metaheuristic algorithms

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2021年第1期65卷 150-167页

作者： Kaveh, Ali Hosseini, Seyed Milad Zaerreza, Ataollah Iran Univ Sci & Technol Sch Civil Engn Tehran *** Iran

The present paper proposes a new strategy namely Boundary Strategy (BS) in the process of optimization-based damage detection using metaheuristic algorithms. This strategy gradually neutralizes the effects of structural elements that are healthy in the optimization process. BS causes the optimization method to find the optimum solution better than conventional methods that do not use the proposed BS. This technique improves both aspects of the accuracy and convergence speed of the algorithms in identifying and quantifying the damage. To evaluate the performance of the developed strategy, a new damage-sensitive cost function, which is defined based on vibration data of the structure, is optimized utilizing the Shuffled Shepherd optimization Algorithm (SSOA). Different examples including truss, beam, and frame are investigated numerically in order to indicate the applicability of the proposed technique. The proposed approach is also applied to other well-known optimization algorithms including TLBO, GWO, and MFO. The obtained results illustrate that the proposed method improves the performance of the utilized algorithms in identifying and quantifying of the damaged elements, even for noise-contaminated data.

关键词： structural damage detection Boundary Strategy metaheuristic optimization algorithms Shuffled Shepherd optimization Algorithm vibration data

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