Metaheuristic optimization algorithms for real-world electrical and civil engineering application: A review

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102437 - 102437

Published: June 18, 2024

Metaheuristic optimization algorithms (MOAs) are gaining increasing interest because of their exceptional effectiveness in addressing many issues. Nevertheless, these often face difficulties when used to solve real-world problems, necessitating further evaluation that problems. In addition, there is currently no established standard for evaluating algorithmic performance. MOAs successfully address engineering challenges due inherent complexity and multidimensional character. Several studies have explored the use tackle electrical civil highlighting practical importance. This study thoroughly examined most recent papers on topics, specifically focusing extraction parameters models.

Language: Английский

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Parameter estimation of ECM model for Li-Ion battery using the weighted mean of vectors algorithm

Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 76, P. 109891 - 109891

Published: Nov. 29, 2023

Language: Английский

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Bald eagle search algorithm: a comprehensive review with its variants and applications

Systems Science & Control Engineering, Journal Year: 2024, Volume and Issue: 12(1)

Published: Aug. 1, 2024

Bald Eagle Search (BES) is a recent and highly successful swarm-based metaheuristic algorithm inspired by the hunting strategy of bald eagles in capturing prey. With its remarkable ability to balance global local searches during optimization, BES effectively addresses various optimization challenges across diverse domains, yielding nearly optimal results. This paper offers comprehensive review research on BES. Beginning with an introduction BES's natural inspiration conceptual framework, it explores modifications, hybridizations, applications domains. Then, critical evaluation performance provided, offering update effectiveness compared recently published algorithms. Furthermore, presents meta-analysis developments outlines potential future directions. As swarm-inspired algorithms become increasingly important tackling complex problems, this study valuable resource for researchers aiming understand algorithms, mainly focusing comprehensively. It investigates evolution, exploring solving intricate fields.

Language: Английский

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Optimal parameter identification strategy applied to lithium-ion battery model for electric vehicles using drive cycle data

Energy Reports, Journal Year: 2024, Volume and Issue: 11, P. 2049 - 2058

Published: Feb. 2, 2024

The optimal parameter identification of lithium-ion (Li-ion) battery models is essential for accurately capturing behavior and performance in electric vehicle (EV) applications. Traditional methods often rely on manual tuning or trial-and-error approaches, which can be time-consuming yield suboptimal results. In recent years, metaheuristic optimization algorithms have emerged as powerful tools efficiently searching identifying values. This paper proposes an strategy using a algorithm applied to Shepherd model EV technique that was based the Self-adaptive Bonobo Optimizer (SaBO) performed extremely well when it came process battery's unidentified properties. Because this, overall voltage error suggested has been lowered 4.2377 × 10−3, root mean square (RMSE) between data calculated 8.64 10−3. addition, compared other methods, efficiency able attain 96.6%, validated its efficiency.

Language: Английский

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Metaheuristics for Solving Global and Engineering Optimization Problems: Review, Applications, Open Issues and Challenges

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(8), P. 4485 - 4519

Published: Aug. 21, 2024

Abstract The greatest and fastest advances in the computing world today require researchers to develop new problem-solving techniques capable of providing an optimal global solution considering a set aspects restrictions. Due superiority metaheuristic Algorithms (MAs) solving different classes problems promising results, MAs need be studied. Numerous studies algorithms fields exist, but this study, comprehensive review MAs, its nature, types, applications, open issues are introduced detail. Specifically, we introduce metaheuristics' advantages over other techniques. To obtain entire view about classifications based on (i.e., inspiration source, number search agents, updating mechanisms followed by agents their positions, primary parameters algorithms) presented detail, along with optimization including both structure types. application area occupies lot research, so most widely used applications presented. Finally, great effort research is directed discuss challenges which help upcoming know future directions active field. Overall, study helps existing understand basic information field addition directing newcomers areas that addressed future.

Language: Английский

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A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems

Sustainability, Journal Year: 2023, Volume and Issue: 15(7), P. 5732 - 5732

Published: March 24, 2023

For many electrical systems, such as renewable energy sources, their internal parameters are exposed to degradation due the operating conditions. Since model’s accuracy is required for establishing proper control and management plans, identifying a critical prominent task. Various techniques have been developed identify these parameters. However, metaheuristic algorithms received much attention use in tackling wide range of optimization issues relating parameter extraction. This work provides an exhaustive literature review on solving extraction utilizing recently algorithms. paper includes newly published articles each studied context its discussion. It aims approve applicability make understanding deployment easier. there not any exact that can offer satisfactory performance all issues, especially problems large search space dimensions. As result, capable searching very spaces possible solutions thoroughly investigated review. Furthermore, depending behavior, divided into four types. These types details included this paper. Then, basics identification process presented discussed. Fuel cells, electrochemical batteries, photovoltaic panel analyzed.

Language: Английский

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Gradient-based optimization for parameter identification of lithium-ion battery model for electric vehicles

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 102845 - 102845

Published: Sept. 4, 2024

Language: Английский

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Multi-Reservoir Flood Control Operation Using Improved Bald Eagle Search Algorithm with ε Constraint Method

Water, Journal Year: 2023, Volume and Issue: 15(4), P. 692 - 692

Published: Feb. 9, 2023

The reservoir flood control operation problem has the characteristics of multiconstraint, high-dimension, nonlinearity, and being difficult to solve. In order better solve this problem, paper proposes an improved bald eagle search algorithm (CABES) coupled with ε-constraint method (ε-CABES). test performance CABES algorithm, a typical function is used simulate verify CABES. results are compared particle swarm optimization its superiority. further rationality effectiveness method, two single reservoirs multi-reservoir system selected for operation, ε constraint penalty (CF-CABES) compared, respectively. Results show that peak clipping rates ε-CABES CF-CABES both 60.28% Shafan Reservoir 52.03% Dahuofang Reservoir, When solving joint system, only successful, rate 51.76%. Therefore, in single-reservoir have similar effects. However, than method. summary, more reliable effective, which provides new scheduling large reservoirs.

Language: Английский

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Predicting slope safety using an optimized machine learning model

Heliyon, Journal Year: 2023, Volume and Issue: 9(12), P. e23012 - e23012

Published: Nov. 29, 2023

The hazards and consequences of slope collapse can be reduced by obtaining a reliable accurate prediction safety, hence, developing effective tools for foreseeing their occurrence is crucial. This research aims to develop state-of-the-art hybrid machine learning approach estimate the factor safety (FOS) earth slopes as precisely possible. current research's contribution body knowledge multifold. In first step, powerful optimization based on artificial electric field algorithm (AEFA), namely global-best (GBAEF), developed verified using number benchmark functions. aim following step utilize technique support vector regression (SVR) predictive model slope's (FOS). Finally, proposed GBAEF employed enhance performance SVR appropriately adjusting hyper-parameters model. implements 153 data sets, including six input parameters one output parameter collected from literature. outcomes show that implementing efficient algorithms adjust greatly accuracy. A case study Chamoli District, Uttarakhand used compare traditional stability techniques. According experimental findings, new AI has improved FOS accuracy about 7% when compared other forecasting models. also optimized with performs wonderfully in disciplines training testing, maximum R2 0.9633 0.9242, respectively, which depicts significant connection between observed anticipated FOS.

Language: Английский

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Reduced-order reconstruction of discrete grey forecasting model and its application

Communications in Nonlinear Science and Numerical Simulation, Journal Year: 2024, Volume and Issue: 139, P. 108310 - 108310

Published: Aug. 24, 2024

Language: Английский

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