Data Clustering with Grasshopper Optimization Algorithm

Łukasik, Szymon; Kowalski, Piotr A.; Charytanowicz, Małgorzata; Kulczycki, Piotr

doi:10.15439/2017f340

Cited by 40 publications

(14 citation statements)

References 18 publications

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“…Łukasik et al [147] used GOA for generating accurate data clusterings. GOA was validated using benchmark datasets based on real-world examples taken from the UCI machine learning repository.…”

Section: A Goa For Continuous Optimization 1) Clusteringmentioning

confidence: 99%

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

et al. 2021

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Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

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Section: A Goa For Continuous Optimization 1) Clusteringmentioning

confidence: 99%

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

et al. 2021

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“…Neelam Rajput in 2017 introduced an ideal age booking of warm framework utilizing organically roused grasshopper algorithm [49].This paper illuminate three distinct sorts of ED issues comprising little, medium and enormous scope power framework with various intricacy levels.The execution of GOA is very prevalent for tackling ED issues. Baran Hekimoglu in 2018 proposed a GOA for automatic voltage regulation syatem [50].The simplicity of this algorithm gives a high quality tunung of optimal PID(proportional integral derivative) controller parameters. The algorithm used is specified below:…”

Section: Ai= μEwmentioning

confidence: 99%

Analysis of Various Optimization Techniques in Machine Learning

Kaur¹

2021

TURCOMAT

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Optimization model the goal of solving a task in the paramount way providing the best results and it implies a best view point for solving query or we can also define optimization as a way of probing an another solution with the maximum cost efficient and utmost practicable performance under the required constraints, by increasing the preferred factor and reducing the unprefferable ones. This increase provides the utmost result or inference without any obstacles or complications. In this paper, various optimization techniques with respect to image processing are studied, where image processing deals with certain tasks and they are Image segmentation, image compression, image enhancement, image restoration (in painting).This paper provides basic knowledge of some optimization techniques.

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“…The GOA has fewer adjustable parameters to be set and has a fast convergence speed. Therefore, GOA has been successfully adapted for numerous optimization problems, include cloud logistics [61], clustering [62], image processing [63], global optimization problems [64], and classification tasks [65]. Due to the efficacy of GOA in various applications, that motivates numerous researchers towards enhancing the optimization ability of the basic GOA by hybridizing it with other meta-heuristic algorithms or local search algorithms [63,66,67] and modification of some components of GOA [66,68,69], have been proposed to address different complex optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Multiple Filter-Based Rankers to Guide Hybrid Grasshopper Optimization Algorithm and Simulated Annealing for Feature Selection With High Dimensional Multi-Class Imbalanced Datasets

Sharifai

Zainol

2021

IEEE Access

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DNA microarray data analysis is infamous due to a massive number of features, imbalanced class distribution, and limited available samples. In this paper, we focus on high-dimensional multi-class imbalanced problems. The high dimensional and multi-class imbalanced problem has posed acute challenges for the conventional classifiers to effectively perform classification tasks on both the minority and majority classes. Numerous efforts have been devoted to addressing either high dimensionality dataset or class imbalance problems. Nonetheless, few methods have been proposed to address the intersection of multi-class imbalanced and high-dimensional problems concurrently due to their intricate interactions. This paper presents novel hybrid algorithms for feature selection with the high dimensional multi-class imbalanced problem using multiple filter-based rankers (MFR) and hybrid Grasshopper optimization algorithm (GOA). The Simulated Annealing (SA) algorithm is incorporated into GOA. SA is used to enhance the best solution found by the GOA algorithm. The aim of using the SA here is to tackle the slow convergence and improve the exploitation by searching the high-quality regions found by the GOA. The experimental results confirm the effectiveness of the proposed methods in improving the classification performance in terms of area under the curve (AUC) compared to other well-known methods, which guarantees the ability of the proposed methods in searching the feature space and identifying very robust and discriminative features that best predict the minority class.

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Data Clustering with Grasshopper Optimization Algorithm

Cited by 40 publications

References 18 publications

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Analysis of Various Optimization Techniques in Machine Learning

Multiple Filter-Based Rankers to Guide Hybrid Grasshopper Optimization Algorithm and Simulated Annealing for Feature Selection With High Dimensional Multi-Class Imbalanced Datasets

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