A novel harmony search-based approach for clustering problems

Senthilnath, J.; Kulkarni, Sushant; Raghuram, D.R.; Sudhindra, Meghana; Omkar, S. N.; Das, Vipul; Mani, V.

doi:10.1504/ijsi.2016.077434

Cited by 9 publications

(5 citation statements)

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“…Lévy flight (LF) [30] is a random walk mode between short-range search and occasional long-range search. Similarly, researchers found that the Lévy flight can also improve the performance of nature-inspired algorithms [46,47].…”

Section: Lévy Flight Distribution Introduced To Tsamentioning

confidence: 95%

Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight

Chen¹,

Przystupa²,

Ye³

et al. 2022

Eksploatacja i Niezawodność – Maintenance and Reliability

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In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. In this paper, a hybrid method, composed of kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed for short-term load forecasting. Specifically, the randomly generated weights and biases of ELM have a significant impact on the stability of prediction results. Therefore, in order to solve this problem, LTSA is utilized to obtain the optimal parameters before the prediction process is executed by ELM, which is called LTSA-ELM. Meanwhile, the input data is extracted by KPCA considering the sparseness of the electric load data and used as the input of LTSA-ELM model. The proposed method is tested on the data from European network on intelligent technologies (EUNITE) and experimental results demonstrate the superiority of the proposed approaches compared to the other methods involved in the paper.

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Section: Lévy Flight Distribution Introduced To Tsamentioning

confidence: 95%

Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight

Chen¹,

Przystupa²,

Ye³

et al. 2022

Eksploatacja i Niezawodność – Maintenance and Reliability

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“…In [92], Senthilnath et al proposed a new clustering approach, based on the HSA, to obtain cluster centers and consequently to design the statistics. Three commonly used datasets from the UCI repository and real images were applied to confirm the results obtained using the proposed approach.…”

Section: Image Clustering Applicationsmentioning

confidence: 99%

A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications

2020

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The Harmony Search Algorithm (HSA) is a swarm intelligence optimization algorithm which has been successfully applied to a broad range of clustering applications, including data clustering, text clustering, fuzzy clustering, image processing, and wireless sensor networks. We provide a comprehensive survey of the literature on HSA and its variants, analyze its strengths and weaknesses, and suggest future research directions.

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“…Thus, it has been employed by many animals when foraging foods, such as albatrosses and fruit flies, and spider monkeys (Edwards et al 2007). As a matter of fact, some previous efforts found that Levy flight could improve the performance of nature-inspired algorithms (Senthilnath et al 2013;Senthilnath et al 2016).…”

Section: Levy Flightmentioning

confidence: 99%

A novel unsupervised Levy flight particle swarm optimization (ULPSO) method for multispectral remote-sensing image classification

Zhang

et al. 2017

International Journal of Remote Sensing

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A novel harmony search-based approach for clustering problems

Cited by 9 publications

References 0 publications

Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight

Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight

A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications

A novel unsupervised Levy flight particle swarm optimization (ULPSO) method for multispectral remote-sensing image classification

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