Utilizing the Honeybees Mating-Inspired Firefly Algorithm to Extract Parameters of the Wind Speed Weibull Model DOI Creative Commons
Abubaker Younis,

Fatima Belabbes,

Petru Adrian Cotfas

et al.

Forecasting, Journal Year: 2024, Volume and Issue: 6(2), P. 357 - 377

Published: May 22, 2024

This study introduces a novel adjustment to the firefly algorithm (FA) through integration of rare instances cannibalism among fireflies, culminating in development honeybee mating-based (HBMFA). The IEEE Congress on Evolutionary Computation (CEC) 2005 benchmark functions served as rigorous testing ground evaluate efficacy new diverse optimization scenarios. Moreover, thorough statistical analyses, including two-sample t-tests and fitness function evaluation analysis, algorithm’s capabilities were robustly validated. Additionally, coefficient determination, used an objective function, was utilized with real-world wind speed data from SR-25 station Brazil assess applicability modeling parameters. Notably, HBMFA achieved superior solution accuracy, enhancements averaging 0.025% compared conventional FA, despite moderate increase execution time approximately 18.74%. Furthermore, this dominance persisted when performance other common algorithms. However, some limitations exist, longer HBMFA, raising concerns about its practical scenarios where computational efficiency is critical. while demonstrates improvements values, establishing significance these differences FA not consistently achieved, which warrants further investigation. Nevertheless, added value work lies advancing state-of-the-art algorithms, particularly enhancing accuracy for critical engineering applications.

Language: Английский

A Global Best-guided Firefly Algorithm for Engineering Problems DOI
Mohsen Zare, Mojtaba Ghasemi,

Amir Zahedi

et al.

Journal of Bionic Engineering, Journal Year: 2023, Volume and Issue: 20(5), P. 2359 - 2388

Published: May 17, 2023

Language: Английский

Citations

111
Red-tailed hawk algorithm for numerical optimization and real-world problems DOI Creative Commons
Seydali Ferahtia, Azeddine Houari, Hegazy Rezk

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Aug. 9, 2023

This study suggests a new nature-inspired metaheuristic optimization algorithm called the red-tailed hawk (RTH). As predator, has hunting strategy from detecting prey until swoop stage. There are three stages during process. In high soaring stage, explores search space and determines area with location. low moves inside selected around to choose best position for hunt. Then, swings hits its target in stooping swooping stages. The proposed mimics prey-hunting method of solving real-world problems. performance RTH been evaluated on classes first class includes specific kinds problems: 22 standard benchmark functions, including unimodal, multimodal, fixed-dimensional multimodal IEEE Congress Evolutionary Computation 2020 (CEC2020), CEC2022. is compared eight recent algorithms confirm contribution these considered Farmland Fertility Optimizer (FO), African Vultures Optimization Algorithm (AVOA), Mountain Gazelle (MGO), Gorilla Troops (GTO), COOT algorithm, Hunger Games Search (HGS), Aquila (AO), Harris Hawks (HHO). results regarding accuracy, robustness, convergence speed. second seven engineering problems that will be investigate other published profoundly. Finally, proton exchange membrane fuel cell (PEMFC) extraction parameters performed evaluate complex problem. several papers approve performance. ultimate each ability provide higher most cases. For class, mostly got optimal solutions functions faster provided better third when resolving real word or extracting PEMFC parameters.

Language: Английский

Citations

53
A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks DOI Creative Commons
Mohaimenul Azam Khan Raiaan, Sadman Sakib, Nur Mohammad Fahad

et al.

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 11, P. 100470 - 100470

Published: April 24, 2024

Convolutional Neural Network (CNN) is a prevalent topic in deep learning (DL) research for their architectural advantages. CNN relies heavily on hyperparameter configurations, and manually tuning these hyperparameters can be time-consuming researchers, therefore we need efficient optimization techniques. In this systematic review, explore range of well used algorithms, including metaheuristic, statistical, sequential, numerical approaches, to fine-tune hyperparameters. Our offers an exhaustive categorization (HPO) algorithms investigates the fundamental concepts CNN, explaining role variants. Furthermore, literature review HPO employing above mentioned undertaken. A comparative analysis conducted based strategies, error evaluation accuracy results across various datasets assess efficacy methods. addition addressing current challenges HPO, our illuminates unresolved issues field. By providing insightful evaluations merits demerits objective assist researchers determining suitable method particular problem dataset. highlighting future directions synthesizing diversified knowledge, survey contributes significantly ongoing development optimization.

Language: Английский

Citations

48
Chaotic Sand Cat Swarm Optimization DOI Creative Commons
Farzad Kiani, Sajjad Nematzadeh,

Fateme Aysin Anka

et al.

Mathematics, Journal Year: 2023, Volume and Issue: 11(10), P. 2340 - 2340

Published: May 17, 2023

In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This combines the features of recently introduced SCSO with concept chaos. The basic aim to integrate chaos feature non-recurring locations into SCSO’s core search process improve global performance convergence behavior. Thus, randomness in can be replaced by chaotic map due similar better statistical dynamic properties. addition these advantages, low consistency, local optimum trap, inefficiency search, population diversity issues are also provided. CSCSO, several maps implemented more efficient behavior exploration exploitation phases. Experiments conducted on wide variety well-known test functions increase reliability results, as well real-world was applied total 39 multidisciplinary It found 76.3% responses compared best-developed variant other chaotic-based metaheuristics tested. extensive experiment indicates that CSCSO excels providing acceptable results.

Language: Английский

Citations

42
Optimization DC-DC boost converter of BLDC motor drive by solar panel using PID and firefly algorithm DOI Creative Commons
Izza Anshory, Jamaaluddin Jamaaluddin, Arief Wisaksono

et al.

Results in Engineering, Journal Year: 2023, Volume and Issue: 21, P. 101727 - 101727

Published: Dec. 30, 2023

The use of solar photovoltaic panels as source power for Brushless Direct Current (BLDC) motors requires a DC-DC Converter circuit. One application energy is motors. main problem the voltage fluctuation and low DC generated by panel. This research aims to improve performance Boost circuit minimize fluctuations. methodology encompasses mathematical modeling in form transfer functions optimizing using Proportional Integral Derivative (PID) controller Firefly algorithm. Simulation testing results indicate an improvement transient response driver BLDC motor. evidenced increase rise time from 499 s 820 s, decrease settling 3.33 e+03 2.07e+03 reduction overshoot 0 % previously 11.4 %. utilization firefly algorithm optimization significantly enhances system efficiency, demonstrated faster achievement stability without excessive oscillation required settle. Overall, this study shows that effective developing circuits, improving efficiency reducing eliminating overshoot. These findings provide empirical evidence effectiveness artificial intelligence algorithms enhancing operational conversion systems.

Language: Английский

Citations

34
A Novel Bio-Inspired Optimization Algorithm Design for Wind Power Engineering Applications Time-Series Forecasting DOI Creative Commons
Faten Khalid Karim, Doaa Sami Khafaga, Marwa M. Eid

et al.

Biomimetics, Journal Year: 2023, Volume and Issue: 8(3), P. 321 - 321

Published: July 20, 2023

Wind patterns can change due to climate change, causing more storms, hurricanes, and quiet spells. These changes dramatically affect wind power system performance predictability. Researchers practitioners are creating advanced forecasting algorithms that combine parameters data sources. Advanced numerical weather prediction models, machine learning techniques, real-time meteorological sensor satellite used. This paper proposes a Recurrent Neural Network (RNN) model incorporating Dynamic Fitness Al-Biruni Earth Radius (DFBER) algorithm predict patterns. The of this is compared with several other popular including BER, Jaya Algorithm (JAYA), Fire Hawk Optimizer (FHO), Whale Optimization (WOA), Grey Wolf (GWO), Particle Swarm (PSO)-based models. evaluation done using various metrics such as relative root mean squared error (RRMSE), Nash Sutcliffe Efficiency (NSE), absolute (MAE), bias (MBE), Pearson’s correlation coefficient (r), determination (R2), agreement (WI). According the analysis presented in study, proposed RNN-DFBER-based outperforms models considered. suggests RNN model, combined DFBER algorithm, predicts effectively than alternative To support findings, visualizations provided demonstrate effectiveness RNN-DFBER model. Additionally, statistical analyses, ANOVA test Wilcoxon Signed-Rank test, conducted assess significance reliability results.

Language: Английский

Citations

28
A multi-objective thermal exchange optimization model for solving optimal power flow problems in hybrid power systems DOI Creative Commons
Sunilkumar Agrawal, Sundaram B. Pandya, Pradeep Jangir

et al.

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 8, P. 100299 - 100299

Published: Aug. 9, 2023

This study addresses the challenges associated with optimal power flow (OPF) management in hybrid systems incorporating diverse energy sources, particularly focusing on unpredictability of renewable sources (RESs). A novel analytics approach is introduced using Multi-Objective Thermal Exchange Optimization (MOTEO). MOTEO based modeling transfer grounded Newton's Law Cooling. The model integrates innovative non-dominated sorting and crowing distance strategies to effectively solve multi-objective optimization problem. proposed OPF incorporates four primary types resources: thermal, wind, solar, small-hydro, offering a holistic systems. Our model's practical applicability efficiency are validated through rigorous testing modified IEEE 30-Bus system, benchmarked against other contemporary methodologies. results demonstrate that successfully identifies solutions for (MOOPF) problem while maintaining compliance stringent system constraints. contribution enhances field by providing robust efficient handle complex systems, thereby ensuring increased reliability.

Language: Английский

Citations

27
Chaotic Harris Hawks Optimization Algorithm for Electric Vehicles Charge Scheduling DOI Creative Commons
V. Manoj Kumar,

C. Bharatiraja,

Ali Elrashidi

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 11, P. 4379 - 4396

Published: April 16, 2024

Electric Vehicle (EV) technology and migration are hindered by battery sizing, short driving ranges, optimal operations. This article focuses on developing a strategy for scheduling EV charging in specific region, addressing waiting time, uneven due to unevenly distributed stations (CS). The proposed approach optimizes CS using separate queues different levels, reducing time costs during peak hours. Which considers trade-offs between time-aware fairness overall factors like reachability, state of charge, depth discharge limits, rate constraints. A bi-objective formulation online algorithm based dynamic schedulable energy demand fluctuation user's prioritization proposed. aim is allocate station each considering travel needs specifics, with the objective minimizing queue recharging costs. To achieve this, system utilizes Chaotic Harris Hawks Optimization (CHHO), an enhanced iteration previously discussed metaheuristic, Hawk Optimization. Validation conducted through Vehicular Ad-hoc Network (VANET) simulation comparison alternative algorithms Exponential Optimization, Grey Wolf Optimizer Random allocation. outcomes demonstrate noteworthy decreases costs, all while adhering set

Language: Английский

Citations

13
A comprehensive review of dwarf mongoose optimization algorithm with emerging trends and future research directions DOI Creative Commons

Olanrewaju L. Abraham,

Md Asri Ngadi

Decision Analytics Journal, Journal Year: 2025, Volume and Issue: unknown, P. 100551 - 100551

Published: Feb. 1, 2025

Language: Английский

Citations

1
Optimal fog node selection based on hybrid particle swarm optimization and firefly algorithm in dynamic fog computing services DOI
Sunday Oyinlola Ogundoyin, Ismaila Adeniyi Kamil

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 121, P. 105998 - 105998

Published: March 1, 2023

Language: Английский

Citations

21