Integrating Differential Evolution into Gazelle Optimization for advanced global optimization and engineering applications

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 434, С. 117588 - 117588

Опубликована: Ноя. 29, 2024

Язык: Английский

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Improved Golden Jackal Optimization for Optimal Allocation and Scheduling of Wind Turbine and Electric Vehicles Parking Lots in Electrical Distribution Network Using Rosenbrock’s Direct Rotation Strategy

Mathematics, Год журнала: 2023, Номер 11(6), С. 1415 - 1415

Опубликована: Март 15, 2023

In this paper, a multi-objective allocation and scheduling of wind turbines electric vehicle parking lots are performed in an IEEE 33-bus radial distribution network to reach the minimum annual costs power loss, purchased grid energy, PHEV battery degradation cost, voltage deviations. Decision variables, such as site size system, found using improved golden jackal optimization (IGJO) algorithm based on Rosenbrock’s direct rotational (RDR) strategy. The results showed that IGJO finds optimal solution with lower convergence tolerance better (lower) objective function value compared conventional GJO, artificial field (AEFA), particle swarm (PSO), manta ray foraging (MRFO) methods. proposed method IGJO, energy loss deviations were reduced by 29.76%, 65.86%, 18.63%, respectively, base network. Moreover, statistical analysis proved their superiority AEFA, PSO, MRFO algorithms. considering vehicles costs, losses have been 3.28%, 1.07%, 4.32%, case without costs. addition, decrease availability causes increasing for weakens profile, vice versa.

Язык: Английский

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Fractional order controller design via gazelle optimizer for efficient speed regulation of micromotors

e-Prime - Advances in Electrical Engineering Electronics and Energy, Год журнала: 2023, Номер 6, С. 100295 - 100295

Опубликована: Сен. 22, 2023

In this paper, the design of an efficient fractional-order proportional-integral-derivative (FOPID) controller, tailored specifically for regulation micro direct current (DC) motors, is explored. A fresh approach introduced using gazelle optimization algorithm (GOA), a cutting-edge method set to manage speed control these small but vital motors. With adoption GOA, fine-tuning parameters FOPID controller aimed. This achieved by employing time-based performance metrics-based cost function as guiding compass. The GOA implemented discover optimal settings attainment peak performance. Through thorough simulations and careful statistical analysis, worth GOA-based demonstrated. Not only are top-notch values achieved, excellence across various time domain-based metrics also excelled in. results suggest that proves be in parameters. comprehensive analysis domain further solidifies superiority FOPID-controlled micro-DC motor system. Outperformance observed when compared alternative algorithms different controllers, such advanced hybrid stochastic fractal search-based grey wolf optimizer-based slime mold algorithm-based PID enhanced arithmetic Harris hawks optimization-based atom search simulated annealing-based controller. essence, highlight potential powerful tool poised reshape controllers regulation.

Язык: Английский

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An information gap decision theory and improved gradient-based optimizer for robust optimization of renewable energy systems in distribution network

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 2, 2025

In this paper, a robust fuzzy multi-objective framework is performed to optimize the dispersed and hybrid renewable photovoltaic-wind energy resources in radial distribution network considering uncertainties of generation demand. A novel improved gradient-based optimizer (MOIGBO) enhanced with Rosenbrock's direct rotational technique overcome premature convergence proposed determine problem optimal decision variables. The deterministic optimization without uncertainty minimizes active loss, unmet customer energy, costs. study also examines impact on solving problem. obtained results, focus determining maximum radius (MUR) resource demand based risk. MURs system robustness are optimally determined using information gap theory (IGDT) MOIGBO, various budgets under worst-case scenarios. results indicate that MOIGBO effectively balances objectives identifies final solution within Pareto front, according decision-making. reveal case yields better objective values than case. Furthermore, outperforms MOGBO particle swarm (MOPSO) improving operations. show achieved at 30% risk due forecasting errors, MUR 0.54% for production 12.56% load

Язык: Английский

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A New Cloud-Stochastic Framework for Optimized Deployment of Hydrogen Storage in Distribution Network Integrated with Renewable Energy Considering Hydrogen-Based Demand Response

Energy, Год журнала: 2025, Номер 316, С. 134483 - 134483

Опубликована: Янв. 13, 2025

Язык: Английский

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Improving Safety and Efficiency of Industrial Vehicles by Bio‐Inspired Algorithms

Expert Systems, Год журнала: 2025, Номер 42(3)

Опубликована: Янв. 22, 2025

ABSTRACT In the context of industrial automation, optimising automated guided vehicle (AGV) trajectories is crucial for enhancing operational efficiency and safety. They must travel in crowded work areas cross narrow corridors with strict safety time requirements. Bio‐inspired optimization algorithms have emerged as a promising approach to deal complex scenarios. Thus, this paper explores ability three novel bio‐inspired algorithms: Bat Algorithm (BA), Whale Optimization (WOA) Gazelle (GOA); optimise AGV path planning environments. To do it, new strategy described: trajectory based on clothoid curves specialised piece‐wise fitness function which prioritises designed. Simulation experiments were conducted across different occupancy maps evaluate performance each algorithm. WOA demonstrates faster providing suitable solutions 4 times than GOA. Meanwhile, GOA gives better metrics but demands more computational time. The study highlights potential approaches optimisation suggests avenues future research, including hybrid algorithm development.

Язык: Английский

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Enhancing stochastic planning in autonomous hybrid energy systems through an advanced arithmetic optimization algorithm and K-means data clustering

Energy Reports, Год журнала: 2025, Номер 13, С. 4375 - 4387

Опубликована: Апрель 11, 2025

Язык: Английский

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Multi skill project scheduling optimization based on quality transmission and rework network reconstruction

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 19, 2025

Quality deficiencies are widely acknowledged as a primary driver of project rework, with personnel skill levels serving critical determinant activity quality. This study presents scheduling model that integrates quality transmission mechanisms and dynamic rework subnet reconstruction within the Multi-Skill Resource-Constrained Project Scheduling Problem (MSRCPSP) framework. The proposed aims to optimize duration while mitigating risks. To address computational complexity model, an Improved Gazelle Optimization Algorithm (GOAIP) was developed, incorporating operators, shuffle crossover, Gaussian mutation strategies balance global local optimization. Experimental validation across diverse case scales demonstrates algorithm outperform mainstream optimization techniques in solution accuracy convergence efficiency, highlighting their robust applicability practical significance.

Язык: Английский

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Early energy performance analysis of smart buildings by consolidated artificial neural network paradigms

Heliyon, Год журнала: 2024, Номер 10(4), С. e25848 - e25848

Опубликована: Фев. 1, 2024

The assessment of energy performance in smart buildings has emerged as a prominent area research driven by the increasing consumption trends worldwide. Analyzing attributes using optimized machine learning models been highly effective approach for estimating cooling load (CL) and heating (HL) buildings. In this study, an artificial neural network (ANN) is used basic predictor that undergoes optimization five metaheuristic algorithms, namely coati algorithm (COA), gazelle (GOA), incomprehensible but intelligible-in-time logics (IbIL), osprey (OOA), sooty tern (STOA) to predict CL HL residential building. are trained tested via Energy Efficiency dataset (downloaded from UCI Repository). A score-based ranking system built upon three accuracy evaluators including mean absolute percentage error (MAPE), root square (RMSE), percentage-Pearson correlation coefficient (PPCC) compare prediction models. Referring results, all demonstrated high (e.g., PPCCs >89%) predicting both HL. However, calculated final scores (43, 20, 39, 38, 10 36, 42, STOA, OOA, IbIL, GOA, COA, respectively) indicated STOA perform better than COA OOA. Moreover, comparison with various algorithms earlier literature showed provide more accurate solution. Therefore, use ANN these recommended practical early forecast optimizing design systems.

Язык: Английский

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CB-HDM: ECG signal based heart disease classification using convolutional block attention assisted hybrid deep Maxout network

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 95, С. 106388 - 106388

Опубликована: Май 3, 2024

Язык: Английский

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