Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems

Jiang, Li; Guo, Lihong; Yan, Li; Liu, Chang

doi:10.3390/math7050395

Cited by 33 publications

(16 citation statements)

References 117 publications

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“…The numerical results contain three sets of various test functions; Unimodal set (F1-F5), Multimodal set (F6-F8) and Composite set (F9 & F10) [17,[28][29][30]. Each test was repeated 30 times in order to obtain reliable statistical results for 10 mathematical benchmark functions with the average best (AB), the median best (MB) and the standard deviation (SD) that are tabulated as in Table 2.…”

Section: Numerical Results For Performance Evaluation Of Algorithmsmentioning

confidence: 99%

Speed controller design for three-phase induction motor based on dynamic adjustment grasshopper optimization algorithm

Algamluoli

Abbas

2021

IJECE

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Three-phase induction motor (TIM) is widely used in industrial application like paper mills, water treatment and sewage plants in the urban area. In these applications, the speed of TIM is very important that should be not varying with applied load torque. In this study, direct on line (DOL) motor starting without controller is modelled to evaluate the motor response when connected directly to main supply. Conventional PI controller for stator direct current and stator quadrature current of induction motor are designed as an inner loop controller as well as a second conventional PI controller is designed in the outer loop for controlling the TIM speed. Proposed combined PI-lead (CPIL) controllers for inner and outer loops are designed to improve the overall performance of the TIM as compared with the conventional controller. In this paper, dynamic adjustment grasshopper optimization algorithm (DAGOA) is proposed for tuning the proposed controller of the system. Numerical results based on well-selected test function demonstrate that DAGOA has a better performance in terms of speed of convergence, solution accuracy and reliability than SGOA. The study results revealed that the currents and speed of TIM system using CPIL-DAGOA are faster than system using conventional PI and CPIL controllers tuned by SGOA. Moreover, the speed controller of TIM system with CPIL controlling scheme based on DAGOA reached the steady state faster than others when applied load torque.

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Section: Numerical Results For Performance Evaluation Of Algorithmsmentioning

confidence: 99%

Speed controller design for three-phase induction motor based on dynamic adjustment grasshopper optimization algorithm

Algamluoli

Abbas

2021

IJECE

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“…Here, a ∈ (1, H ) is updated every time, with the exception of the elephant of a particular clan that has a pathetic fit. The equation for elephant b in clan CN a is expressed as follows 14 :

{CC}_{new, {CN}_{a}, b} = {CC}_{{CN}_{a}, b} + η \times ((), {CC}_{best, {CN}_{a}} - {CC}_{{CN}_{a}, b}) \times τ

…”

Section: Implementation Of the Proposed Image Segmentation Methodsmentioning

confidence: 99%

Modified transform‐based gamma correction for MRI tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm

Kollem

Reddy²,

Rao

2020

Int J Imaging Syst Tech

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Medical image processing is typically performed to diagnose a patient's brain tumor prior to surgery. In this study, a technique in denoising and segmentation was developed to improve medical image processing. The proposed approach employs multiple modules. In the first module, the noisy brain tumor image is transformed into multiple low‐ and high‐pass tetrolet coefficients. In the second module, multiple low‐pass tetrolet coefficients are applied through a modified transform‐based gamma correction method. Generalized cross‐validation is used on multiple high‐pass tetrolet coefficients to obtain the best threshold value. In the third module, all enhanced coefficients are applied to the partial differential equation method. In the final module, the denoised image is applied to Atanassov's intuitionistic fuzzy set histon‐based fuzzy clustering method with centroid optimization using an elephant herding method. Accordingly, the tumor part is segmented from the nontumor part in the magnetic resonance imaging brain images. The method was assessed in terms of peak signal‐to‐noise ratio, mean square error, specificity, sensitivity, and accuracy. The experimental results showed that the suggested method is superior to traditional methods.

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“…EHO algorithm. A fundamental EHO algorithm is explained by utilizing the following principles 29 1. Elephants belong to various clans and live jointly lead by a fitting elephant.…”

Section: Eho-based Fs Processmentioning

confidence: 99%

Computational Intelligence-Based Financial Crisis Prediction Model Using Feature Subset Selection with Optimal Deep Belief Network

et al. 2021

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At present times, financial decisions are mainly based on the classifier technique, which is utilized to allocate a collection of observations into fixed groups. A diverse set of data classifier approaches were presented for forecasting the financial crisis of an institution using the past data. An essential process toward the design of a precise financial crisis prediction (FCP) approach comprises the choice of proper variables (features) that are related to the issues at hand. This is termed as a feature selection (FS) issue that assists to improvise the classifier results. Besides, computational intelligence techniques can be used as a classification model to determine the financial crisis of an organization. In this view, this article introduces a new FS using elephant herd optimization (EHO) with modified water wave optimization (MWWO) algorithm-based deep belief network (DBN) for FCP. The EHO algorithm is applied as a feature selector, and MWWO-DBN is utilized for the classification process. The application of the MWWO algorithm helps to tune the parameters of the DBN model, and the choice of optimal feature subset from the EHO algorithm leads to enhanced classification performance. The experimental results of the proposed model are tested against three benchmark data sets, namely AnalcatData, German Credit, and Australian Credit. The obtained simulation results indicated the superior performance of the proposed model by attaining maximum classification performance.

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Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems

Cited by 33 publications

References 117 publications

Speed controller design for three-phase induction motor based on dynamic adjustment grasshopper optimization algorithm

Speed controller design for three-phase induction motor based on dynamic adjustment grasshopper optimization algorithm

Modified transform‐based gamma correction for MRI tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm

Computational Intelligence-Based Financial Crisis Prediction Model Using Feature Subset Selection with Optimal Deep Belief Network

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