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

Hybrid firefly algorithm with a new mechanism of gender distinguishing for global optimization DOI
Zhiwen Cheng, Haohao Song,

Debin Zheng

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 224, P. 120027 - 120027

Published: April 1, 2023

Language: Английский

Citations

17
Optimizing transmission line parameter estimation with hybrid evolutionary techniques DOI Creative Commons

Muhammad Suhail Shaikh,

Saurav Raj, Shah Abdul Latif

et al.

IET Generation Transmission & Distribution, Journal Year: 2024, Volume and Issue: 18(9), P. 1795 - 1814

Published: April 13, 2024

Abstract Power flow, planning, economics, dispatch, and stability analysis rely on accurate transmission line parameters (TLPE). Standard optimization methods are employed to develop such analyses obtain TLPE. Additionally, these have limitations, including precision, accuracy, time complexity. It is challenging find improved solutions using standard due slow convergence limitations in identifying local optima. Concerned with challenges, the study suggest a new application for an effective hybrid method capable of addressing limitations. The algorithm, named Salp Swarm Algorithm Sine Cosine (HSSASCA), that aims tackle issues (SCA) after (SSA), integration utilized successfully explore analyze search space. To enhance performance HSSASCA, technique provide expanded exploration capabilities, exploitation space, better rate. These key features position HSSASCA algorithm as solution complex problems. assess efficiency six different test systems employed. Initially, evaluation exploration, exploitation, minimized optima conducted CEC 2019 benchmark functions. Secondly, monitoring verification across scenarios occur by comparing it established algorithms SSA, SCA, firefly (FFO), Grey Wolf Optimization (GWO), student psychology‐based (SPBO), Symbiotic Organisms Search (SOS). Finally, statistical performed, revealing outperforms FFO, GWO, SPBO, SOS. In terms results curves, demonstrates superior searching efficiency, optimum avoidance ability.

Language: Английский

Citations

8
A new approach for active and reactive power management in renewable based hybrid microgrid considering storage devices DOI
Kanche Anjaiah, P.K. Dash, Ranjeeta Bisoi

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 367, P. 123429 - 123429

Published: May 15, 2024

Language: Английский

Citations

8
A modified Particle Swarm Optimization algorithm with enhanced search quality and population using Hummingbird Flight patterns DOI Creative Commons
Mohsen Zare,

Mohammad‐Amin Akbari,

Rasoul Azizipanah‐Abarghooee

et al.

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 7, P. 100251 - 100251

Published: May 12, 2023

This study proposes a modified Particle Swarm Optimization (PSO) algorithm based on Hummingbird Flight (HBF) patterns to enhance the search quality and population diversity. The HBF has five concepts: (1) Smaller steps toward position updating are more likely than larger ones, (2) Position changes made step by throughout flight, (3) energy is conserved during nectar-searching process, (4) Hummingbirds do not fly in large groups confined spaces, (5) Simultaneous all directions realistic. A comprehensive two CEC-2010 CEC-2013 benchmark suites conducted verify effectiveness of proposed PSO-HBF algorithm. also evaluated compared other well-known PSO algorithms using shifted rotated CEC 2005 2014 functions. Four cases economic dispatch, 10-unit reserve constraint, 30-unit dynamic dispatch (DED) further examined. last investigate how deals with large-scale practical problems. results demonstrated that superior seven algorithms, improving eight ten functions 2010 2013 benchmarks, respectively. Furthermore, achieving third rank among nineteen improved confirms Moreover, DED problem, show significant improvement over previously published papers. algorithm's source code can be accessed publicly at http://www.optim-app.com/projects/psohbf.

Language: Английский

Citations

16
Forecasting solar power generation using evolutionary mating algorithm-deep neural networks DOI Creative Commons
Mohd Herwan Sulaiman, Zuriani Mustaffa

Energy and AI, Journal Year: 2024, Volume and Issue: 16, P. 100371 - 100371

Published: April 17, 2024

This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases deep neural networks (DNN) for forecasting solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC output using real plant measurements spanning 34-day period, recorded at 15-minute intervals. intricate nonlinear relationship between irradiation, ambient temperature, module temperature is captured accurate prediction. Additionally, conducts comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search (HSA-DNN), DNN Adaptive Moment Estimation optimizer (ADAM) Nonlinear AutoRegressive eXogenous inputs (NARX). experimental results distinctly highlight exceptional performance EMA-DNN by attaining lowest Root Mean Squared Error (RMSE) during testing. contribution not only advances methodologies but also underscores potential merging algorithms contemporary improved accuracy reliability.

Language: Английский

Citations

6
A new flower pollination algorithm with improved convergence and its application to engineering optimization DOI Creative Commons

Kok Meng Ong,

Pauline Ong, Sia Chee Kiong

et al.

Decision Analytics Journal, Journal Year: 2022, Volume and Issue: 5, P. 100144 - 100144

Published: Nov. 21, 2022

The flower pollination algorithm (FPA) is a nature-inspired optimization that mimics the behaviour of flowering plants. Despite promising performance FPA in solving single objective problems, its convergence still poses challenges practice. This study proposes modified with additional features from chaos theory and frog leaping augmented by inertia weights. proposed this tested against benchmark mathematical functions, mechanical engineering design machining process problems. Performance comparison other state-of-the-art algorithms has demonstrated ability terms convergence. significantly reduced number function evaluations 84.14%, as compared to optimizing functions. Besides, outperformed others 12 out 15

Language: Английский

Citations

21
An improved artificial rabbits optimization for accurate and efficient infinite impulse response system identification DOI Creative Commons
Rizk M. Rizk‐Allah, Serdar Ekinci, Davut İzci

et al.

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 9, P. 100355 - 100355

Published: Nov. 3, 2023

Identifying models with Infinite Impulse Response (IIR) is crucial in signal processing and system identification. This paper addresses the challenges of IIR model identification by proposing an improved version Artificial Rabbits Optimization (ARO) algorithm called ARO (IARO). The IARO integrates adaptive local search mechanism experience-based perturbed learning strategy as two key enhancements to improve effectiveness ARO. These additions aim address loss accuracy during iterations algorithm's ability exploit promising areas. Four benchmark examples different plants are considered, performance proposed compared existing competitive methods. results consistently demonstrate that outperforms convergence for across all orders systems. Visual analysis, curves, coefficient comparison, statistical metrics comparison validate superiority algorithm. Additionally, Wilcoxon signed-rank test provide further evidence supporting superior IARO. comprehensive analysis showcases efficacy accurately identifying work represents a significant advancement identification, offering methodology accurate efficient modeling.

Language: Английский

Citations

12
Optimal reconfiguration design and HIL validation of hybrid PV-TEG systems via improved firefly algorithm DOI
Bo Yang, Zijian Zhang, Jie Zhang

et al.

Energy, Journal Year: 2023, Volume and Issue: 286, P. 129648 - 129648

Published: Nov. 16, 2023

Language: Английский

Citations

12
A self-adaptable Manta ray optimized Gabor filter for satellite image enhancement DOI
Anju Asokan

Earth Science Informatics, Journal Year: 2023, Volume and Issue: 16(2), P. 1503 - 1517

Published: March 8, 2023

Language: Английский

Citations

11
Learning search algorithm: framework and comprehensive performance for solving optimization problems DOI Creative Commons
Chiwen Qu, Xiaoning Peng,

Qilan Zeng

et al.

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(6)

Published: May 9, 2024

Abstract In this study, the Learning Search Algorithm (LSA) is introduced as an innovative optimization algorithm that draws inspiration from swarm intelligence principles and mimics social learning behavior observed in humans. The LSA optimizes search process by integrating historical experience real-time information, enabling it to effectively navigate complex problem spaces. By doing so, enhances its global development capability provides efficient solutions challenging tasks. Additionally, improves collective capacity incorporating teaching active behaviors within population, leading improved local capabilities. Furthermore, a dynamic adaptive control factor utilized regulate algorithm’s exploration abilities. proposed rigorously evaluated using 40 benchmark test functions IEEE CEC 2014 2020, compared against nine established evolutionary algorithms well 11 recently algorithms. experimental results demonstrate superiority of algorithm, achieves top rank Friedman rank-sum test, highlighting power competitiveness. Moreover, successfully applied solve six real-world engineering problems 15 UCI datasets feature selection problems, showcasing significant advantages potential for practical applications problems.

Language: Английский

Citations

4