Data Clustering Using Harmony Search Algorithm

Alia, Osama Moh’d; Al-Betar, Mohammed Azmi; Rajeswari, M.; Khader, Ahamad Tajudin

doi:10.1007/978-3-642-27242-4_10

Cited by 28 publications

(9 citation statements)

References 21 publications

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“…Even the precise difference between these versions and the ImHS variant will be highlighted. The work done in [23], shows an approach which consists of two stages. In the first stage, HS explores the search space to find out the near-optimal cluster centers (HS-c means).…”

Section: State Of the Art In Hs Versions Focused On Parameters Improvements For The Last 10 Yearsmentioning

confidence: 99%

A Comparative Study of Improved Harmony Search Algorithm in Four Bar Mechanisms

et al. 2020

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There are problems that are difficult to solve through mathematical programming or by classical methods. These problems are called hard problems due to their high complexity or high dimension. On the other hand, mataheuristics intends to seek a better solution to a problem. The Improvement Harmony Search algorithm is proposed under modification of the bandwidth parameter increasing the quality of the exploitation of the solutions. That is why within the state of the art, are mentioned several versions of harmonic search. The state of the art is supports the fact that the algorithm belongs to the category of those who make modifications to its parameters. This research demonstrates the ability of ImHS to solve a problem of high complexity focused on solving four-bar mechanism designs, whose solutions imply high dimension and which are also classified as hard problems. The two problems that are solved in this investigation, are problems very attacked within the state of the art by various metaheuristics. A comparison is then made against previous solutions with traditional metaheuristics and other versions of harmony search algorithm. Finally, the effectiveness of performance is demonstrated, where proposed algorithm it exceeded five metaheuristic algorithms and five harmony search versions. An optimum is provided in an easy and useful way, the parametric statistics are improved and the number of feasible solutions is exceeded in NPhard problems as in the case of problems with four-bar mechanisms.INDEX TERMS algorithms performance, four-bar mechanism, improved harmony search, optimization problems, mechatronic.

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Section: State Of the Art In Hs Versions Focused On Parameters Improvements For The Last 10 Yearsmentioning

confidence: 99%

A Comparative Study of Improved Harmony Search Algorithm in Four Bar Mechanisms

et al. 2020

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“…Harmony search (HS) algorithm is a stochastic new population-based meta-heuristic approach introduced in (2001) [21]. It imitates the music improvisation process where the musicians improvise their instruments pitch by searching for the optimal state of harmony.…”

Section: Harmony Search Algorithmmentioning

confidence: 99%

“…end if 12: end for HS solutions generated according to the PAR(I) and bw(I) parameters. If generated a random value between [0, 1] is less or equal PAR [19,21], than the new decision variable (x) is determine as follows:…”

Section: Harmony Search Algorithmmentioning

confidence: 99%

“…Nature meta-heuristic algorithms have been used in many areas such as com-puter science, data mining, agriculture, computer vision, forecasting, medicine and biology, economy and engineering [8]. Recently, many meta-heuristic algo-rithms have been used to improve the data clustering technique such as k-mean [2], artificial bee colony (ABC) [6], bee colony optimization (BCO) [7], tabu search approach (TS) [4], particle swarm optimization (PSO) [8,24] and harmony search (HS) algorithms [17,19,21,25].…”

Section: Introductionmentioning

confidence: 99%

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

Abualigah¹,

Khader²,

Al-Betar³

et al. 2017

Proceedings of the First EAI International Conference on Computer Science and Engineering

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Krill herd (KH) is a stochastic nature-inspired algorithm, it has been successfully used to solve many complex optimization problems. The performance of krill herd algorithm (KHA) is effected by poor exploitation capability. This paper proposes new data clustering algorithm based on a hybrid of krill herd algorithm (KHA) and harmony search (HS) algorithm (Harmony-KHA) in order to improve the data clustering technique. This hybrid strategy seeking to enhance the global search capability of the KHA. The enhancement includes of adding global search operator from HS algorithm for exploration around the optimal solution in KH and thus kill individuals move towards the global best solution. The proposed method is applied to preserve the best krill individual during the krill position update. Experiments were conducted using four standard datasets from the UCI Machine Learning Repository, which is used in the domain of data clustering. The results showed that the proposed hybrid KHA and HS algorithm (Harmony-KHA) is produced very accurate clusters, especially in the large dataset.

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“…The algorithm was inspired by both researches on the behavior of real ant colonies and some data mining concepts as well as principles. In another study, Moh'd Alia et al [8] employed the harmony search algorithm. They compared their method with random initialization mode.…”

Section: Introductionmentioning

confidence: 99%

Using Metaheuristic Algorithms to Improve k-Means Clustering: A Comparative Study

Harifi¹,

Khalilian²,

Mohammadzadeh³

et al. 2020

RIA

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Finding the most suitable centroids for k-means clustering is one of the most important criteria for successful clustering operation. We are always looking for the best centroids. Since, clustering problem and finding best centroids are an NP-hard problems, using metaheuristic algorithms can be an appropriate tool to deal with these issues. Many authors have solved this issue with metaheuristic algorithms. Common and popular algorithms have very good solutions. But which of the metaheuristic algorithms really provides the best solution? To answer this question, in this comparative study, ten popular metaheuristic algorithms are compared. The comparisons are performed on synthetic and ten real-world datasets. To find significant differences between the results obtained by algorithms, statistical analysis is used. Comparison results are presented with suitable tables.

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Data Clustering Using Harmony Search Algorithm

Cited by 28 publications

References 21 publications

A Comparative Study of Improved Harmony Search Algorithm in Four Bar Mechanisms

A Comparative Study of Improved Harmony Search Algorithm in Four Bar Mechanisms

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

Using Metaheuristic Algorithms to Improve k-Means Clustering: A Comparative Study

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