A new firefly algorithm with improved global exploration and convergence with application to engineering optimization

Ghasemi, Maryam; Mohammadi, Soleiman Kadkhoda; Zare, Mohsen; Mirjalili, Seyedali; Gil, Milad; Hemmati, Rasul

doi:10.1016/j.dajour.2022.100125

Cited by 31 publications

(15 citation statements)

References 126 publications

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“…In the study, the investigation’s findings are thoroughly examined, focusing on the DFBER algorithm. This algorithm is analyzed and compared to other state-of-the-art algorithms including BER [ 31 ], Jaya Algorithm (JAYA) [ 32 ], Fire Hawk Optimizer (FHO) [ 33 ], Whale Optimization Algorithm (WOA) [ 34 ], Grey Wolf Optimizer (GWO) [ 35 ], Particle swarm optimization (PSO) [ 36 ], Firefly algorithm (FA) [ 37 ], and Genetic Algorithm (GA) [ 35 ]. The DFBER algorithm’s configuration is presented in Table 1 .…”

Section: Resultsmentioning

confidence: 99%

“…This study suggests a Recurrent Neural Network (RNN) forecasting model based on a proposed Dynamic Fitness Al-Biruni Earth Radius (DFBER) algorithm for capturing and predicting data patterns. We compare its performance with popular models such as BER [ 31 ], Jaya Algorithm (JAYA) [ 32 ], Fire Hawk Optimizer (FHO) [ 33 ], Whale Optimization Algorithm (WOA) [ 34 ], Grey Wolf Optimizer (GWO) [ 35 ], Particle swarm optimization (PSO) [ 36 ], Firefly algorithm (FA) [ 37 ], and Genetic Algorithm (GA) [ 35 ] based models, utilizing a comprehensive range of evaluation metrics. The evaluation includes metrics like relative root mean squared error (RRMSE), Nash Sutcliffe Efficiency (NSE), mean absolute error (MAE), mean bias error (MBE), Pearson’s correlation coefficient (r), coefficient of determination (R2), and determine agreement (WI), along with considerations of dataset size, estimated and observed values, and arithmetic means of bandwidths.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Bio-Inspired Optimization Algorithm Design for Wind Power Engineering Applications Time-Series Forecasting

et al. 2023

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

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Bio-Inspired Optimization Algorithm Design for Wind Power Engineering Applications Time-Series Forecasting

et al. 2023

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“…The hybrid firefly and particle swarm optimization (HFPSO) method is a new approach to optimizing ensemble classifiers that associates the firefly algorithm [27] and PSO [28] to discover the best arrangement of individual classifiers in an ensemble. The firefly algorithm is an optimization technique inspired by the flashing patterns of fireflies, while the PSO is a bio-inspired optimization method that mimics the behaviour of flocks of birds or swarms of insects.…”

Section: Classificationmentioning

confidence: 99%

A Novel Approach of DDOS Attack Classification with Optimizing the Ensemble Classifier Using A Hybrid Firefly and Particle Swarm Optimization (HFPSO)

2023

IJIES

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Cyberspace is fraught with dangers, one of which is the distributed denial of service attack (DDoS). This type of attack is particularly concerning as it can disrupt vital services, prevent authorized users from accessing them, and result in financial losses. The aim of this research is to present an optimized AdaBoost classifier that has been fine-tuned using the HFPSO algorithm. The data is pre-processed to ensure that it conforms to standard features by normalizing it. Additionally, cross-correlation techniques are used to select features in order to eliminate redundancy. Finally, the constructed signals are used to train and test an HFPSO-optimized AdaBoost classifier. The result indicates the possibility of anticipating the attacks is fairly accurate. The system accuracy is 99.97%.

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“…Benchmark methods such as Salp Swarm and Sine Cosine Optimization have been verified in different power system applications, including power flow analysis [21,22], parameter estimation [23,24], parameter extraction of solar photovoltaic models using rat swarm optimization [25], power system stabilizer [26], stability improvement [27], and optimization of retaining walls [28,29]. Popular optimization techniques such as Whale Optimization Algorithm (WOA) [30], Grey Wolf Optimization (GWO) [31], Student Psychology-Based Optimization (SPBO) [32], Symbiotic Organisms Search (SOS) [33], and Firefly algorithm (FFO) [34] have been introduced, offering alternative approaches to address these challenges. Choosing an optimization method that is both efficient and time-effective for calculating TLPEs in 3φ systems is a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Optimizing transmission line parameter estimation with hybrid evolutionary techniques

Shaikh,

Raj,

Abdul Latif

et al. 2024

IET Generation Trans & Dist

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

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A new firefly algorithm with improved global exploration and convergence with application to engineering optimization

Cited by 31 publications

References 126 publications

A Novel Bio-Inspired Optimization Algorithm Design for Wind Power Engineering Applications Time-Series Forecasting

A Novel Bio-Inspired Optimization Algorithm Design for Wind Power Engineering Applications Time-Series Forecasting

A Novel Approach of DDOS Attack Classification with Optimizing the Ensemble Classifier Using A Hybrid Firefly and Particle Swarm Optimization (HFPSO)

Optimizing transmission line parameter estimation with hybrid evolutionary techniques

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