Optimizing FACTS Device Placement Using the Fata Morgana Algorithm: A Cost and Power Loss Minimization Approach in Uncertain Load Scenario-Based Systems

International Journal of Computational Intelligence Systems, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 20, 2025

Language: Английский

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Multi-objective Newton-Raphson-Based Optimizer for Fractional-Order Control of PEM Fuel Cells

Results in Engineering, Journal Year: 2025, Volume and Issue: 25, P. 104152 - 104152

Published: Jan. 23, 2025

Language: Английский

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Prediction of viscosity of blast furnace slag based on NRBO-DNN model

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 119, P. 124 - 137

Published: Feb. 2, 2025

Language: Английский

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Tornado optimizer with Coriolis force: a novel bio-inspired meta-heuristic algorithm for solving engineering problems

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(4)

Published: Feb. 5, 2025

Language: Английский

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Fungal growth optimizer: A novel nature-inspired metaheuristic algorithm for stochastic optimization

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2025, Volume and Issue: 437, P. 117825 - 117825

Published: Feb. 9, 2025

Language: Английский

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Parameters identification of photovoltaic models using Lambert W-function and Newton-Raphson method collaborated with AI-based optimization techniques: A comparative study

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124777 - 124777

Published: July 14, 2024

Accurately estimating the unknown parameters of photovoltaic (PV) models based on measured voltage-current data is a challenging optimization problem due to its high nonlinearity and multimodality. An accurate solution this essential for efficiently simulating, controlling, evaluating PV systems. There are three different models, including single-diode model, double-diode triple-diode with five, seven, nine parameters, respectively, proposed represent electrical characteristics systems varying levels complexity accuracy. In literature, several deterministic metaheuristic algorithms have been used accurately solve hard problem. However, problem, methods could not achieve solutions. On other side, algorithms, also known as gradient-free methods, somewhat good solutions but they still need further improvements strengthen their performance against stuck-in local optima slow convergence speed problems. Over last two years, recent better improve avoid tackle continuous majority those has investigated. Therefore, in paper, nineteen recently published such Mantis search algorithm (MSA), spider wasp optimizer (SWO), light spectrum (LSO), growth (GO), walrus (WAOA), hippopotamus (HOA), black-winged kite (BKA), quadratic interpolation (QIO), sinh cosh (SCHA), exponential distribution (EDO), optical microscope (OMA), secretary bird (SBOA), Parrot Optimizer (PO), Newton-Raphson-based (NRBO), crested porcupine (CPO), differentiated creative (DCS), propagation (PSA), one-to-one (OOBO), triangulation topology aggregation (TTAO), studied clarify effectiveness models. addition, collaborate functions, namely Lambert W-Function Newton-Raphson Method, aid solving I-V curve equations more accurately, thereby improving Those assessed using four well-known solar cells modules compared each metrics, best fitness, average worst standard deviation (SD), Friedman mean rank, speed; multiple-comparison test compare difference between ranks. Results comparison show that SWO efficient effective SDM, DDM, TDM over modules, Method equations. study reports perform poorly when applied

Language: Английский

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An efficient approach to parameter extraction of photovoltaic cell models using a new population-based algorithm

Applied Energy, Journal Year: 2024, Volume and Issue: 364, P. 123208 - 123208

Published: April 13, 2024

Language: Английский

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DEMFFA: a multi-strategy modified Fennec Fox algorithm with mixed improved differential evolutionary variation strategies

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: May 8, 2024

Abstract The Fennec Fox algorithm (FFA) is a new meta-heuristic that primarily inspired by the fox's ability to dig and escape from wild predators. Compared with other classical algorithms, FFA shows strong competitiveness. “No free lunch” theorem an has different effects in face of problems, such as: when solving high-dimensional or more complex applications, there are challenges as easily falling into local optimal slow convergence speed. To solve this problem FFA, paper, improved Fenna fox DEMFFA proposed adding sin chaotic mapping, formula factor adjustment, Cauchy operator mutation, differential evolution mutation strategies. Firstly, mapping strategy added initialization stage make population distribution uniform, thus speeding up Secondly, order expedite speed algorithm, adjustments made factors whose position updated first stage, resulting faster convergence. Finally, prevent getting too early expand search space population, after second stages original update. In verify performance DEMFFA, qualitative analysis carried out on test sets, tested newly algorithms three sets. And we also CEC2020. addition, applied 10 practical engineering design problems 24-bar truss topology optimization problem, results show potential problems.

Language: Английский

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Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

Cognitive Computation, Journal Year: 2020, Volume and Issue: 12(5), P. 897 - 939

Published: July 5, 2020

Language: Английский

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Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 9, 2024

Language: Английский

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