The variants of the harmony search algorithm: an overview

Alia, Osama Moh’d; Rajeswari, M.

doi:10.1007/s10462-010-9201-y

Cited by 215 publications

(85 citation statements)

References 76 publications

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“…There are different variants of this algorithm that have been created to improve it or solve specific problems [19,[28][29][30]. This method is composed of the following steps:…”

Section: Harmony Search Algorithmmentioning

confidence: 99%

Optimization of Intelligent Controllers Using a Type-1 and Interval Type-2 Fuzzy Harmony Search Algorithm

2017

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This article focuses on the dynamic parameter adaptation in the harmony search algorithm using Type-1 and interval Type-2 fuzzy logic. In particular, this work focuses on the adaptation of the parameters of the original harmony search algorithm. At present there are several types of algorithms that can solve complex real-world problems with uncertainty management. In this case the proposed method is in charge of optimizing the membership functions of three benchmark control problems (water tank, shower, and mobile robot). The main goal is to find the best parameters for the membership functions in the controller to follow a desired trajectory. Noise experiments are performed to test the efficacy of the method.

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“…There are different variants of this algorithm that have been created to improve it or solve specific problems [19,[28][29][30]. This method is composed of the following steps:…”

Section: Harmony Search Algorithmmentioning

confidence: 99%

Optimization of Intelligent Controllers Using a Type-1 and Interval Type-2 Fuzzy Harmony Search Algorithm

2017

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“…The solution of an optimization problem using the HS algorithm (shown in Fig. 4) is performed based on following five steps [20,[22][23][24]: Step 4. Update the harmony memory.…”

Section: Harmony Search Algorithmmentioning

confidence: 99%

A New Image Clustering Method Based on the Fuzzy Harmony Search Algorithm and Fourier Transform

2016

J Inf Process Syst

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In the conventional clustering algorithms, an object could be assigned to only one group. However, this is sometimes not the case in reality, there are cases where the data do not belong to one group. As against, the fuzzy clustering takes into consideration the degree of fuzzy membership of each pixel relative to different classes. In order to overcome some shortcoming with traditional clustering methods, such as slow convergence and their sensitivity to initialization values, we have used the Harmony Search algorithm. It is based on the population metaheuristic algorithm, imitating the musical improvisation process. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. We propose in this paper a new unsupervised clustering method called the Fuzzy Harmony Search-Fourier Transform (FHS-FT). It is based on hybridization fuzzy clustering and the harmony search algorithm to increase its exploitation process and to further improve the generated solution, while the Fourier transform to increase the size of the image's data. The results show that the proposed method is able to provide viable solutions as compared to previous work.

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“…In the last years, several researches have focused on developing variants based on the original HS proposal [2]. Some of them have proposed different ways to dynamically set HS parameters, while others presented new improvisation schemes.…”

Section: Evolutionary Optimization Backgroundmentioning

confidence: 99%

Swarm-based Descriptor Combination and its Application for Image Classification

Mansano¹,

Matsuoka²,

Abiuzzi³

et al. 2014

ELCVIA

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In this paper, we deal with the descriptor combination problem in image classification tasks. This problem refers to the definition of an appropriate combination of image content descriptors that characterize different visual properties, such as color, shape and texture. In this paper, we propose to model the descriptor combination as a swarm-based optimization problem, which finds out the set of parameters that maximizes the classification accuracy of the Optimum-Path Forest (OPF) classifier. In our model, a descriptor is seen as a pair composed of a feature extraction algorithm and a suitable distance function. Our strategy here is to combine distance scores defined by different descriptors, as well as to employ them to weight OPF edges, which connect samples in the feature space. An extensive evaluation of several swarm-based optimization techniques was performed. Experimental results have demonstrated the robustness of the proposed combination approach.

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The variants of the harmony search algorithm: an overview

Cited by 215 publications

References 76 publications

Optimization of Intelligent Controllers Using a Type-1 and Interval Type-2 Fuzzy Harmony Search Algorithm

Optimization of Intelligent Controllers Using a Type-1 and Interval Type-2 Fuzzy Harmony Search Algorithm

A New Image Clustering Method Based on the Fuzzy Harmony Search Algorithm and Fourier Transform

Swarm-based Descriptor Combination and its Application for Image Classification

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