Constrained Real-Parameter Optimization Using the Firefly Algorithm and the Grey Wolf Optimizer

2019 ◽  
pp. 155-167 ◽  
Author(s):  
Luis Rodríguez ◽  
Oscar Castillo ◽  
Mario García ◽  
José Soria
Keyword(s):  
Parameter Optimization ◽  
Firefly Algorithm ◽  
Real Parameter ◽  
Grey Wolf Optimizer ◽  
Grey Wolf

scholarly journals TV White Space Network Power Allocation Using Hybrid Grey Wolf Optimizer with Firefly Algorithm and Particle Swarm Optimization

2021 ◽  
Vol 20 ◽  
pp. 66-75
Author(s):  
Kennedy Ronoh ◽  
George Kamucha
Keyword(s):  
Particle Swarm Optimization ◽  
Power Allocation ◽  
Firefly Algorithm ◽  
Population Based ◽  
Metaheuristic Algorithms ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Secondary Users ◽  
Swarm Optimization ◽  
Network Power

TV white spaces (TVWS) can be utilized by Secondary Users (SUs) equipped with cognitive radio functionality on the condition that they do not cause harmful interference to Primary Users (PUs). Optimization of power allocation is necessary when there is a high density of secondary users in a network in order to reduce the level of interference among SUs and to protect PUs against harmful interference. Grey Wolf Optimizer (GWO) is relatively recent population based metaheuristic algorithm that has shown superior performance compared to other population based metaheuristic algorithms. Recent trend has been to hybridize population based metaheuristic algorithms in order to avoid the problem of getting trapped in a local optimum. This paper presents the design and analysis of performance of a hybrid grey wolf optimizer and Firefly Algorithm (FA) with Particle Swarm Optimization operators for optimization of power allocation in TVWS network power allocation as a continuous optimization problem. Matlab was used for simulation. The hybrid of GWO, FA and PSO (HFAGWOPSO) reduces sum power by 81.42% compared to GWO and improves sum throughput by 16.41% when compared to GWO. Simulation results also show that the algorithm has better convergence rate.

scholarly journals Hybrids of Support Vector Regression with Grey Wolf Optimizer and Firefly Algorithm for Spatial Prediction of Landslide Susceptibility

2021 ◽  
Vol 13 (24) ◽  
pp. 4966
Author(s):  
Ru Liu ◽  
Jianbing Peng ◽  
Yanqiu Leng ◽  
Saro Lee ◽  
Mahdi Panahi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Firefly Algorithm ◽  
Spatial Prediction ◽  
Training Dataset ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Conditioning Factors

Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of support vector regression (SVR) with grey wolf optimizer (GWO) and firefly algorithm (FA) by frequency ratio (FR) preprocessed. Therefore, a landslide inventory composed of 140 landslides and 16 landslide conditioning factors is compiled as a landslide database. Among these landslides, 70% (98) landslides were randomly selected as the training dataset of the model, and the other landslides (42) were used to verify the model. The 16 landslide conditioning factors include elevation, slope, aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), normalized difference vegetation index (NDVI), landslide, rainfall, soil and lithology. The conditioning factors selection and spatial correlation analysis were carried out by using the correlation attribute evaluation (CAE) method and the frequency ratio (FR) algorithm. The area under the receiver operating characteristic curve (AUROC) and kappa data of the training dataset and validation dataset are used to evaluate the prediction ability and the relationship between the advantages and disadvantages of landslide susceptibility maps. The results show that the SVR-GWO model (AUROC = 0.854) has the best performance in landslide spatial prediction, followed by the SVR-FA (AUROC = 0.838) and SVR models (AUROC = 0.818). The hybrid models of SVR-GWO and SVR-FA improve the performance of the single SVR model, and all three models have good prospects for regional-scale landslide spatial modeling.

scholarly journals Grey-Wolf-Based Wang’s Demons for Retinal Image Registration

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 659 ◽  
Author(s):  
Sayan Chakraborty ◽  
Ratika Pradhan ◽  
Amira S. Ashour ◽  
Luminita Moraru ◽  
Nilanjan Dey
Keyword(s):  
Image Registration ◽  
Retinal Image ◽  
Firefly Algorithm ◽  
Cuckoo Search ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Swarm Optimization ◽  
Registration Error ◽  
Registration Process ◽  
Fast Processing

Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO)-based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization-based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36 × 10−5.

scholarly journals Grey Wolf Based Wang’s Demons for Retinal Image Registration

2020 ◽  
Author(s):  
Sayan Chakraborty ◽  
Ratika Pradhan ◽  
Amira S. Ashour ◽  
Luminita Moraru ◽  
Nilanjan Dey
Keyword(s):  
Image Registration ◽  
Retinal Image ◽  
Firefly Algorithm ◽  
Cuckoo Search ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Swarm Optimization ◽  
Registration Error ◽  
Registration Process ◽  
Fast Processing

Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO) based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization- based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36×10-5.

scholarly journals Automatic parameterization of Support Vector Machines for Short Texts Polarity Detection

2017 ◽  
Author(s):  
Aurelio Sanabria Rodríguez ◽  
Edgar Casasola Murillo
Keyword(s):  
Support Vector Machines ◽  
Parameter Optimization ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Grid Search ◽  
Classification Models ◽  
Grey Wolf ◽  
Redes Sociales ◽  
Vector Machines ◽  
Toma De Decisiones

Abstract: The information from social media is emerging as a valuable source in decision-making, unfortunately the tools to turn these data into useful information still need some work. Using Support Vector Machines for polarity detection in short texts are popular among researchers for their good results, but parameter optimization to train classification models is a complex and costly process. This article compares two algorithms for automated parameter optimization in the process of creating classification models for polarity detection: the recently created Grey Wolf Optimizer and the Grid Search, using accuracy and f-score metrics.  Spanish Abstract: Los datos provenientes de las redes sociales están emergiendo como una fuente valiosa de información para los procesos de toma de decisiones, desafortunadamente las herramientas para convertir estos datos en información útil todavía tienen mucho camino por recorrer. Utilizar máquinas de soporte vectorial para la detección de polaridad en textos cortos goza de popularidad entre los investigadores debido a sus buenos resultados. Sin embargo, la optimización de los parámetros necesarios para entrenar modelos es un proceso complejo y costoso. Este artículo compara dos algoritmos para la optimización automatizada de parámetros en el proceso de crear modelos de clasificación para la detección de polaridad: Optimizador de lobo gris y las búsqueda en malla, utilizando las métricas de precisión y valor-f.

Sign in / Sign up

Export Citation Format

Share Document