A new hybridization strategy for krill herd algorithm and harmony search algorithm applied to improve the data clustering

Abualigah, Laith; Khader, Ahamad Tajudin; Al-Betar, Mohammed Azmi; Hanandeh, Essam Said

doi:10.4108/eai.27-2-2017.152255

Cited by 16 publications

(7 citation statements)

References 20 publications

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“…Abualigah [53,54] proposed combinations of the harmony search (HS) method and the KHA algorithm to enhance the global search capability, namely H-KHA. The improvement includes adding the HS global search operator to the KHA to enhance the exploration process.…”

Section: Krill Herd Algorithm (Kha)mentioning

confidence: 99%

Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis

et al. 2020

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Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. Moreover, comprehensive experiments are conducted and analyzed to show the performance of the common well-know nature-inspired optimization algorithms in solving the text document clustering problems including Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), and Bat-inspired Algorithm (BA). Seven text benchmark datasets are used to validate the performance of the tested algorithms. The results showed that the performance of the well-known nurture-inspired optimization algorithms almost the same with slight differences. For improvement purposes, new modified versions of the tested algorithms can be proposed and tested to tackle the text clustering problems.

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Section: Krill Herd Algorithm (Kha)mentioning

confidence: 99%

Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis

et al. 2020

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“…HS has been used in many complex problems, and it has produced outstanding results almost every time. Common examples of these problems are healthcare systems [7], fuzzy controllers and benchmark functions [8] [9], beam and ball controllers [10], power flow analysis [11], congestion management [12], data mining [13], job shop scheduling [14], water distribution [6], university timetables [15], structural design, renewable energy [16], data clustering [17], and neural networks [18]. The HS's primary advantages are its clarity in implementation, its track record of success, and its capacity to deal with a wide range of complicated issues at once.…”

Section: Background and Introductionmentioning

confidence: 99%

Harmony search: Current studies and uses on healthcare systems

Abdulkhaleq

Rashid

Alsadoon

et al. 2022

Artificial Intelligence in Medicine

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“…Generally, many clustering algorithms have been developed in such a manner that objects in the same cluster should be similar to each other while objects in different clusters should be dissimilar [3] [4]. Clustering algorithms have been widely applied in solving many problems in various fields such as data analysis [5] [6], data mining [7] [8], machine learning [9], and image retrieval [10]. DNA is the code of life; it is composed of a sequence of four nucleotides.…”

Section: Introductionmentioning

confidence: 99%

Automatic Clustering of DNA Sequences With Intelligent Techniques

2021

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With the discovery of new DNAs, a fundamental problem arising is how to categorize those DNA sequences into correct species. Unfortunately, identifying all data groups correctly and assigning a set of DNAs into k clusters where k must be predefined are one of the major drawbacks in clustering analysis, especially when the data have many dimensions and the number of clusters is too large and hard to guess. Furthermore, finding a similarity measure that preserves the functionality and represents both the composition and distribution of the bases in a DNA sequence is one of the main challenges in computational biology. In this paper, a new soft computing metaheuristic framework is introduced for automatic clustering to generate the optimal cluster formation and to determine the best estimate for the number of clusters. Pulse coupled neural network (PCNN) is utilized for the calculation of DNA sequence similarity or dissimilarity. Bat algorithm is hybridized with the well-known genetic algorithm to solve the automatic data clustering problem. Extensive computational experiments are conducted on the expanded human oral microbiome database (eHOMD). A comparative study between the experimental results shows that the proposed hybrid algorithm achieved superior performance over the standard genetic algorithm and bat algorithm. Moreover, the hybrid performance was compared with competing algorithms from the literature review to ascertain its superiority. Mann-Whitney-Wilcoxon rank-sum test is conducted to statistically validate the obtained clusters.

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A new hybridization strategy for krill herd algorithm and harmony search algorithm applied to improve the data clustering

Cited by 16 publications

References 20 publications

Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis

Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis

Harmony search: Current studies and uses on healthcare systems

Automatic Clustering of DNA Sequences With Intelligent Techniques

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