Statistical analysis of a multi-objective optimization algorithm based on a model of particles with vorticity behavior

Meza, Joaquín; Espitia, Helbert Eduardo; Montenegro, Carlos; Crespo, Rubén González

doi:10.1007/s00500-015-1972-2

Cited by 34 publications

(16 citation statements)

References 29 publications

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“…This propelled to use nature inspired optimization algorithms for WSN as these are robust and effective [9]. These algorithms became popular from the last decade as they can easily adjust to frequently changing environment and have high efficiency [10]. The various algorithms like Particle Swarm Optimization (PSO) [11], Firefly Algorithm (FA) [12] [13], Genetic Algorithm (GA) [14], Grey Wolf Optimization (GWO) [15], Flower pollination Algorithm [16], etc.…”

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confidence: 99%

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Kaur¹,

Arora²

2017

IJIMAI

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Localization is one of the most important factors highly desirable for the performance of Wireless Sensor Network (WSN). Localization can be stated as the estimation of the location of the sensor nodes in sensor network. In the applications of WSN, the data gathered at sink node will be meaningless without localization information of the nodes. Due to size and complexity factors of the localization problem, it can be formulated as an optimization problem and thus can be approached with optimization algorithms. In this paper, the nature inspired algorithms are used and analyzed for an optimal estimation of the location of sensor nodes. The performance of the nature inspired algorithms viz. Flower pollination algorithm (FPA), Firefly algorithm (FA), Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) for localization in WSN is analyzed in terms of localization accuracy, number of localized nodes and computing time. The comparative analysis has shown that FPA is more proficient in determining the coordinates of nodes by minimizing the localization error as compared to FA, PSO and GWO.

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confidence: 99%

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Kaur¹,

Arora²

2017

IJIMAI

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“…Ideally, the selected test functions should have characteristics alike to those of a real-world problem in order to better assess an algorithm [30]. However, no standard benchmark function testbed available.…”

Section: Benchmark Problems and Experiments Settingsmentioning

confidence: 99%

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

Arora¹,

Singh²

2017

IJIMAI

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-In this paper, a new hybrid optimization algorithm which combines the standard Butterfly Optimization Algorithm (BOA) with Artificial Bee Colony (ABC) algorithm is proposed. The proposed algorithm used the advantages of both the algorithms in order to balance the trade-off between exploration and exploitation. Experiments have been conducted on the proposed algorithm using ten benchmark problems having a broad range of dimensions and diverse complexities. The simulation results demonstrate that the convergence speed and accuracy of the proposed algorithm in finding optimal solutions is significantly better than BOA and ABC.

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“…This method has better performance and provides more acceptable responses than statistical algorithms. In Reference [18], the authors describe the development and implementation of feature selection for content-based image retrieval (CBIR) through a system that automatically extracts features from images using color, texture, and shape in order to use feature selection by means of a genetic algorithm that searches for the best feature-use feature selection. The results of this study conclude that the CBIR system is more efficient, and that it performs better when using feature selection based on a genetic algorithm, because it reduces the time for retrieval and also increases the retrieval precision.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter Optimization for Image Recognition over an AR-Sandbox Based on Convolutional Neural Networks Applying a Previous Phase of Segmentation by Color–Space

et al. 2018

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Immersive techniques such as augmented reality through devices such as the AR-Sandbox and deep learning through convolutional neural networks (CNN) provide an environment that is potentially applicable for motor rehabilitation and early education. However, given the orientation towards the creation of topographic models and the form of representation of the AR-Sandbox, the classification of images is complicated by the amount of noise that is generated in each capture. For this reason, this research has the purpose of establishing a model of a CNN for the classification of geometric figures by optimizing hyperparameters using Random Search, evaluating the impact of the implementation of a previous phase of color-space segmentation to a set of tests captured from the AR-Sandbox, and evaluating this type of segmentation using similarity indexes such as Jaccard and Sørensen-Dice. The aim of the proposed scheme is to improve the identification and extraction of characteristics of the geometric figures. Using the proposed method, an average decrease of 39.45% to a function of loss and an increase of 14.83% on average in the percentage of correct answers is presented, concluding that the selected CNN model increased its performance by applying color-space segmentation in a phase that was prior to the prediction, given the nature of multiple pigmentation of the AR-Sandbox.

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Statistical analysis of a multi-objective optimization algorithm based on a model of particles with vorticity behavior

Cited by 34 publications

References 29 publications

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

Hyperparameter Optimization for Image Recognition over an AR-Sandbox Based on Convolutional Neural Networks Applying a Previous Phase of Segmentation by Color–Space

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