Particle distance rank feature selection by particle swarm optimization

Shafipour, Milad; Rashno, Abdolreza; Fadaei, Sadegh

doi:10.1016/j.eswa.2021.115620

Cited by 31 publications

(6 citation statements)

References 44 publications

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“…Throughout the iterative process, particles continually update their positions and velocities based on the interplay between individual and global best values of the current state. Upon locating the present global and individual optimal solutions, particle information is iteratively updated according to the following Equations ( 1)-(4) [30,31]:…”

Section: Ipsomentioning

confidence: 99%

Deep Learning Short Text Sentiment Analysis Based on Improved Particle Swarm Optimization

Yue,

Peng,

Wang

2023

Electronics

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Manually tuning the hyperparameters of a deep learning model is not only a time-consuming and labor-intensive process, but it can also easily lead to issues like overfitting or underfitting, hindering the model’s full convergence. To address this challenge, we present a BiLSTM-TCSA model (BiLSTM combine TextCNN and Self-Attention) for deep learning-based sentiment analysis of short texts, utilizing an improved particle swarm optimization (IPSO). This approach mimics the global random search behavior observed in bird foraging, allowing for adaptive optimization of model hyperparameters. In this methodology, an initial step involves employing a Generative Adversarial Network (GAN) mechanism to generate a substantial corpus of perturbed text, augmenting the model’s resilience to disturbances. Subsequently, global semantic insights are extracted through Bidirectional Long Short Term Memory networks (BiLSTM) processing. Leveraging Convolutional Neural Networks for Text (TextCNN) with diverse convolution kernel sizes enables the extraction of localized features, which are then concatenated to construct multi-scale feature vectors. Concluding the process, feature vector refinement and the classification task are accomplished through the integration of Self-Attention and Softmax layers. Empirical results underscore the effectiveness of the proposed approach in sentiment analysis tasks involving succinct texts containing limited information. Across four distinct datasets, our method attains impressive accuracy rates of 91.38%, 91.74%, 85.49%, and 94.59%, respectively. This performance constitutes a notable advancement when compared against conventional deep learning models and baseline approaches.

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Section: Ipsomentioning

confidence: 99%

Deep Learning Short Text Sentiment Analysis Based on Improved Particle Swarm Optimization

Yue,

Peng,

Wang

2023

Electronics

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“…A two‐stage process that included feature extraction and feature selection was utilized. Shafipour et al (2021) extended one of the most well‐known metaheuristic algorithms, that is, PSO, with a new feature ranking to update the population's position and velocity. The multi‐objective function was used in their work, and the newly proposed method performed better than several start‐of‐the‐art methods.…”

Section: Literature Reviewmentioning

confidence: 99%

A comparison between chaos theory and Lévy flights in sunflower optimization for feature selection

Pereira

Francisco

et al. 2023

Expert Systems

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Feature selection is a knowledge discovery tool to understand the problem by analysing features. In particular, the application of feature selection in data mining can not only improve the quality of extracted patterns and knowledge but also decrease computational costs. Various techniques have been applied to this complex optimization problem, in which metaheuristics have been validated to be superior. This study introduces a new metaheuristic known for having lean and fast programming, inspired by the sunflower's motions for feature selection for the first time. It is equipped with a v‐shaped transfer function and associated with the KNN classifier to become the binary sunflower optimization (BSFO). A total of 12 variants of BSFO are designed based on the chaos theory and Lévy flights, called improved binary sunflower optimization (IBSFO). A discussion between these improvement theories for feature selection has also not been made yet, and it is performed in this paper using 15 benchmark datasets from the UCI repository. The experimental results show that all variants can advance the fitness value of BSFO, and nine of them considerably decrease the computational costs. Furthermore, the chaotic BSFO with the Chebyshev function, taking replacement to normal rand, has the lowest fitness value (−11.37%) and execution time (−9.31%) than the original BSFO. Further, IBSFO is compared with another eight metaheuristics and outperforms these competitors on average fitness value and execution time. Overall, IBSFO proved to find subsets with reduced dimension and high accuracy with meagre computational cost due to its robust explorative and exploitative capacities.

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“…It should also be noted that the use of meta-heuristic algorithms can be a good solution to deal with nonlinear problems in the network. Also, to solve the sizing problems of renewable energy systems, meta-heuristic algorithms and especially PSO algorithm can be an effective solution [20][21][22]. Niazi and Lalwani [23] studied by using the objective function, the microgrid capacity and location optimization are performed using three algorithms, PSO, Genetic Algorithm (GA), and Imperialist Competitive Algorithm (ICA), for a 13 bus radial system.…”

Section: Related Workmentioning

confidence: 99%

Optimal Capacity and Location for Renewable-based Microgrids Considering Economic Planning in Distribution Networks

Aazami,

Dabestani,

Shirkhani

2023

IJE

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By integrating distributed generation resources with the distribution network, the stability and reliability of the distribution network will increase. Due to the advantages of microgrids and the need to implement them, as well as the high cost of installation of distributed generation resources, the existence of a comprehensive and optimal method by considering various aspects of microgrid design is felt more than ever. In this paper, an optimal method for designing microgrids with various conditions is presented. In the first stage, the design of microgrids is discussed on a multi-purpose basis, considering its economic aspects. At this stage, to make a compromise between the economic aspects, the proposed problem is modeled as two-objective functions. In the second stage, the design of distributed generation sources is done in the first level and then in the second level, the optimal placement of switches is done to determine the electrical boundaries of microgrids. In this paper, the discussion of optimal microgrid location based on economic planning using a two-level particle swarm optimization (PSO) algorithm on the standard IEEE 33 Bus network in MATLAB software was performed and for this network, three microgrids with two keys were used.

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Particle distance rank feature selection by particle swarm optimization

Cited by 31 publications

References 44 publications

Deep Learning Short Text Sentiment Analysis Based on Improved Particle Swarm Optimization

Deep Learning Short Text Sentiment Analysis Based on Improved Particle Swarm Optimization

A comparison between chaos theory and Lévy flights in sunflower optimization for feature selection

Optimal Capacity and Location for Renewable-based Microgrids Considering Economic Planning in Distribution Networks

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