Stability Investigation of Improved Whale Optimization Algorithm in the Process of Feature Selection

Khaire, Utkarsh Mahadeo; Dhanalakshmi, R.

doi:10.1080/02564602.2020.1843554

Cited by 14 publications

(8 citation statements)

References 39 publications

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“…One of the most important aspects of an optimizer is its computational stability. Stability refers to the optimizer's ability to generate the same solution for the same problem, starting from different initial values [41]. The proposed PSO-GD algorithm was tested for stability on the IEEE 33 and 69 test RDN.…”

Section: Stability Analysis Of Pso-gdmentioning

confidence: 99%

An Efficient Hybrid Particle Swarm and Gradient Descent Method for the Estimation of the Hosting Capacity of Photovoltaics by Distribution Networks

2023

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With many distribution networks adopting photovoltaic (PV) generation systems in their networks, there is a significant risk of over-voltages, reverse power flow, line congestion, and increased harmonics. Therefore, there is a need to estimate the amount of PV that can be injected into the distribution network without pushing the network towards these threats. The largest amount of PV a distribution system can accommodate is the PV hosting capacity (PVHC). The paper proposes an efficient method for estimating the PVHC of distribution networks that combines particle swarm optimization (PSO) and the gradient descent algorithm (GD). PSO has a powerful exploration of the solution space but poor exploitation of the local search. On the other hand, GD has great exploitation of local search to obtain local optima but needs better global search capabilities. The proposed method aims to harness the advantages of both PSO and GD while alleviating the ills of each. The numerical case studies show that the proposed method is more efficient, stable, and superior to the other meta-heuristic approaches.

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Section: Stability Analysis Of Pso-gdmentioning

confidence: 99%

An Efficient Hybrid Particle Swarm and Gradient Descent Method for the Estimation of the Hosting Capacity of Photovoltaics by Distribution Networks

2023

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“…In Binary Cuckoo Search [24] and Improved Binary PSO (Chuang et al, 2008), Altruistic Whale Optimization Algorithm (AltWOA) (Kundu et al, 2022), improved Salp Swarm Algorithm (iSSA) (Balakrishnan et al, 2021), improved WOA (iWOA) (Khaire & Dhanalakshmi, 2022), the algorithms utilized search spaces and converted them into a binary vector in each dimension by a sigmoid transfer function, as shown in Eq. ( 3).…”

Section: S-shaped Transfer Functionmentioning

confidence: 99%

A Survey of Feature Selection Strategies for DNA Microarray Classification

2023

CEIS

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Classification tasks are difficult and challenging in the bioinformatics field, that used to predict or diagnose patients at an early stage of disease by utilizing DNA microarray technology. However, crucial characteristics of DNA microarray technology are a large number of features and small sample sizes, which means the technology confronts a "dimensional curse" in its classification tasks because of the high computational execution needed and the discovery of biomarkers difficult. To reduce the dimensionality of features to find the significant features that can employ feature selection algorithms and not affect the performance of classification tasks. Feature selection helps decrease computational time by removing irrelevant and redundant features from the data. The study aims to briefly survey popular feature selection methods for classifying DNA microarray technology, such as filters, wrappers, embedded, and hybrid approaches. Furthermore, this study describes the steps of the feature selection process used to accomplish classification tasks and their relationships to other components such as datasets, crossvalidation, and classifier algorithms. In the case study, we chose four different methods of feature selection on two-DNA microarray datasets to evaluate and discuss their performances, namely classification accuracy, stability, and the subset size of selected features.

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“…Low solution precision, poor local search performance, and sluggish convergence rate are limitations of GWO. An improved Whale Optimization Algorithm (iWOA) was proposed, which implements the exploitation phase using Singer's equation (Khaire and Dhanalakshmi, 2022). It has shown good stability for the selected feature subset.…”

Section: → →mentioning

confidence: 99%

Stable Feature Selection using Improved Whale Optimization Algorithm for Microarray Datasets

Theng,

Bhoyar

2023

ADCAIJ

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A microarray is a collection of DNA sequences that reflect an organism’s whole gene set and are organized in a grid pattern for use in genetic testing. Microarray datasets are extremely high-dimensional and have a very small sample size, posing the challenges of insufficient data and high computational complexity. Identification of true biomarkers that are the most significant features (a very small subset of the complete feature set) is desired to solve these issues. This reduces over-fitting, and time complexity, and improves model generalization. Various feature selection algorithms are used for this biomarker identification. This research proposed a modification to the whale optimization algorithm (WOAm) for biomarker discovery, in which the fitness of each search agent is evaluated using the hinge loss function during the hunting for prey phase to determine the optimal search agent. Also compared the results of the proposed modified algorithm with the original whale optimization algorithm and also with contemporary algorithms like the marine predator algorithm and grey wolf optimization. All these algorithms are evaluated on six different high-dimensional microarray datasets. It has been observed that the proposed modification for the whale optimization algorithm has significantly improved the results of feature selection across all the datasets. Domain experts trust the resultant biomarker/ associated genes by the stability of the results obtained. The chosen feature set’s stability was also evaluated during the research work. According to the findings, our proposed WOAm has superior stability compared to other algorithms for the CNS, colon, Leukemia, and OSCC. datasets.

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Stability Investigation of Improved Whale Optimization Algorithm in the Process of Feature Selection

Cited by 14 publications

References 39 publications

An Efficient Hybrid Particle Swarm and Gradient Descent Method for the Estimation of the Hosting Capacity of Photovoltaics by Distribution Networks

An Efficient Hybrid Particle Swarm and Gradient Descent Method for the Estimation of the Hosting Capacity of Photovoltaics by Distribution Networks

A Survey of Feature Selection Strategies for DNA Microarray Classification

Stable Feature Selection using Improved Whale Optimization Algorithm for Microarray Datasets

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