A Novel Hybrid Bird Mating Optimizer with Differential Evolution for Engineering Design Optimization Problems

Sadeeq, Haval Tariq; Abdulazeez, Adnan Mohsin; Kako, Najdavan A.; Abrahim, Araz Rajab

doi:10.1007/978-3-319-59427-9_55

Cited by 14 publications

(10 citation statements)

References 21 publications

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“…When implementing machine learning algorithms for sentiment analysis, those documents with label information must be used to train a predictive model. This same information can also help feature extraction methods create excellent features [45,50].…”

Section: Feature Extractionmentioning

confidence: 94%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

Self Cite

611

210

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Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

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Section: Feature Extractionmentioning

confidence: 94%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

Self Cite

611

210

View full text Add to dashboard Cite

show abstract

“…It separated (N) data objects into (K) cluster sets to obtain low cross similarity and high intracluster specificity. The mean diameter of a cluster's points, also known as the cluster's centroid, is used to evaluate cluster similarity [36,37,38]. It goes like this: first, choose k points at random as the cluster's mean (center).…”

Section: A Clustering Algorithmsmentioning

confidence: 99%

COVID-19 World Vaccine Adverse Reactions Based on Machine Learning Clustering Algorithm

Abdulrahman

Abdulazeez

Hasan

2021

QAJ

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In December 2019, a novel coronavirus, now named SARS-CoV-2, caused a series of acute atypical respiratory diseases in Wuhan, Hubei province, China. It triggered several acute atypical respiratory diseases. COVID-19 was the name given to the virus's disease. The infection is human-to-human transmissible, and it has triggered a global pandemic. Vaccines against COVID-19 are an essential global intervention to control the current pandemic situation and in a fairly short time, several vaccines have been developed to try to control the situation but have also led to consequences in the form of adverse reactions. Clustering algorithms have been used in computational intelligence and digital analysis, which is one of the areas that has taken this into account. Clustering can be described as a method of grouping similar data into one population or cluster and separating unrelated data into another. For clustering COVID-19 vaccine adverse reactions datasets, a variety of clustering algorithms are used. The objective of this paper is to use the clustering algorithms used in the case of COVID-19 Vaccine Adverse Reactions datasets, demonstrating how these algorithms help to provide accuracy for clustering the COVID-19 Vaccine Adverse Reactions. This study compared four clustering algorithms using the WEKA tool. Furthermore, it details the datasets in terms of different variables of precision, cluster case, number of iterations, time, and present the findings of these papers, and which clustering algorithms used and the accuracy of these algorithms. It is found that the clustering algorithm k-means is used widely in different types of the COVID-19 vaccine adverse reactions datasets with high accuracy.

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“…Particle swarm optimization algorithm [8] Termite algorithm [27] Cuckoo search [28] Honey bees optimization algorithm [29] Cockroach swarm optimization [30] Grasshopper optimization algorithm [31] Dolphin partner optimization [32] Fruit fly optimization algorithm [33] Bee collecting pollen algorithm [34] Bird mating optimizer [35] Firefly algorithm [36] Artificial fish-swarm algorithm [37] In the face of so many existing metaoptimization algorithms, a concern naturally rises. So far, there have been many different types of optimization algorithms.…”

Section: Swarmbased Algorithmsmentioning

confidence: 99%

Greenfly Aphid Swarm Optimization Algorithm

Akkar¹,

Salman²

2020

Journal of Southwest Jiaotong University

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A new metaheuristic swarm intelligence optimization technique, called general greenfly aphid swarm optimization algorithm, which is proposed by enhancing the performance of swarm optimization through cockroach swarm optimization algorithm. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization algorithm, cockroach swarm optimization and grasshopper optimization algorithm. Numerical experiments show that the greenfly aphid swarm optimization algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblau’s optimization problem, are also considered and the proposed greenfly aphid swarm optimization algorithm is shown to be competitive in those applications.

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A Novel Hybrid Bird Mating Optimizer with Differential Evolution for Engineering Design Optimization Problems

Cited by 14 publications

References 21 publications

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

COVID-19 World Vaccine Adverse Reactions Based on Machine Learning Clustering Algorithm

Greenfly Aphid Swarm Optimization Algorithm

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