Pelican Optimization Algorithm for Optimal Demand Response in Islanded  Active Distribution Network Considering Controllable Loads

doi:10.22266/ijies2022.1231.14

Cited by 1 publication

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“…Swarm intelligence algorithms have demonstrated their suitability and efficiency for feature selection problems due in particular to their particular in overcoming the curse of dimensionality by optimizing the efficiency of classification, and the quantity of features, and their provide practical solutions in a timely manner [11]. These methods are frequently used to solve different optimization issues [12,13]. Nonetheless, it was also noted that there is room for development, one explanation for this is because many of the suggested metaheuristics experience suffer from an imbalance between exploration and exploitation and stagnation in the local optimum [14].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Feature Selection Approach Based on a Novel Improve Binary Sparrow Search Algorithm for COVID-19 Classification

2023

IJIES

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This paper proposes an improved binary sparrow search algorithm (IBSSA) as a search strategy within the feature selection (FS) methods. Its main objective is to use clinical texts to improve COVID-19 patient categorization. The constant need for an efficient FS system and the favorable outcomes of swarming behavior in numerous optimization situations drove our efforts to develop a novel FS strategy. Additionally, clinical text data are frequently highly dimensional and contain uninformative features, which have a major impact on the classifier's accuracy, making FS a key machine-learning step in data pre-processing to reduce data dimensionality. The bi-stage FS approach is used in this work to elect the features. At the initial stage, we employed a term weighting scheme (TWS) that assigned a weighted score to each feature by measuring the significance of the features obtained from the pre-processing model using a new weight calculation method called root term frequency-core-inverse exponential frequency (RTF-C-IEF). Next, finding the most relevant and almost optimal feature subset for COVID-19 illness diagnosis is done in the second stage using a freshly developed methodology that was inspired by the way sparrow's behavior. The suggested modification method for the sparrow's algorithm is composed of several stages of advancement. The main objectives are to promote the exploration of the search space and increase the algorithm's variability. In order to evaluate the proposed model, various classifiers were employed on two datasets, each of which had 1446 and 3053 cases, respectively. The experimental and statistical results demonstrate that the proposed IBSSA is significantly superior compared to other comparative optimization algorithms, and it successfully upgrades the shortcomings of the original SSA. Moreover, the IBSSA has the highest accurate performance when compared to other rivals by the SVM classifier, Where, average removed features are 77.99% and 83.5%, with improvement percentages by F1-scores: 84.95% and 95.94 % for both datasets, respectively.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Feature Selection Approach Based on a Novel Improve Binary Sparrow Search Algorithm for COVID-19 Classification

2023

IJIES

View full text Add to dashboard Cite

show abstract

Pelican Optimization Algorithm for Optimal Demand Response in Islanded Active Distribution Network Considering Controllable Loads

Cited by 1 publication

References 33 publications

An Intelligent Feature Selection Approach Based on a Novel Improve Binary Sparrow Search Algorithm for COVID-19 Classification

An Intelligent Feature Selection Approach Based on a Novel Improve Binary Sparrow Search Algorithm for COVID-19 Classification

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