An Improved Hill Climbing Search Algorithm for ROSA Coupling

Zhou, Peng; Tang, Yukun; Huang, Qiuyuan; Ma, Chao

doi:10.1109/imcec.2018.8469365

Cited by 6 publications

(3 citation statements)

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“…However, the simplicity also brings disadvantages like easily stacking in local optimal and lack of backtracking. The disadvantages are more obvious in ROSA problem, their inaccuracy and low convergence rate are the two main things researchers need to focus on and solve [14].…”

Section: Definition and Mechanismmentioning

confidence: 99%

A review of metaheuristic optimization methods

Cui

2024

ACE

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In this paper, the metaheuristic optimization methods are categorized. Each method is explained detailly in basic principle and working mechanism. The article is separated to four parts. The second part shows the principle and implementation for each method such as GA, PSO, ACO and TS. The algorithms are categorized according to their searching type. The third part shows the combination of different optimization methods used to solve complex, multi-objective, high-dimensional problems. The fourth part is the future and challenges for metaheuristic optimization. The paper includes recent experiments to 2022.

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Section: Definition and Mechanismmentioning

confidence: 99%

A review of metaheuristic optimization methods

Cui

2024

ACE

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“…It will fine-tune randomly selected element in X at all iteration and furthermore computes the modification to develop the value of the given function. This iterative procedure is repeated till convergence i.e., no further modification in thefunction [2]. If the algorithm starts with a poor location, the algorithm may be converging to the undesirable solution i.e., local maximum.PFCM clustering is a combination of fuzzy c-means (FCM) and possiblistic c-means (PCM) clusteing.PFCM provided answer to numerous issues of both FCM and PCM.The foremost inconvenience of FCM is that its noise sensitivity i.e., it treats noisy pixel as original one.…”

Section: Color Image Segmentationmentioning

confidence: 99%

Automatic Segmentation of Natural Color Images in CIE Lab Space using Possibilistic Fuzzy C Means Clustering

Kalist¹,

Joe²,

Vishnupriya³

et al. 2019

IJITEE

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Clustering is the most significant assignment in image processing. This work performs the segmentation of natural color images in CIELab space based on the Possibilistic fuzzy c means clustering (PFCM).The basic principle of the proposed approach is the segmentation of natural color images based on the two-way approach of hill climbing (HC) and PFCM. In this work, RGB image is transformed into CIELab space for the efficient extraction of the secreted treasure in the images. The combined approach of local optimization search technique, HC and PFCM is applied for the segmentation of synthetic fiber images. This color histogram based technique works on the principle of identification of peaks in the color histogram of the natural color image. The identified peaks are considered as initial seed or clusters. These seeds are then applied to the PFCM to perform the final segmentation. Investigational outcomedemonstrates the competence of thetwo-way approach of HC and PFCMwhich presents the preeminentend result for less complexity color images.

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“…The essential points dealt with in this study consist of the modeling of a photovoltaic system integrating the MPPT by using of Arduino board in realtime simulation. By considering a comparison of obtained results of the proposed method with other some maximum power point tracking techniques namely P&O, INC [13], fractional short-circuit current (FSCC) [14], fraction algorithm open circuit voltage (FCO) [15], hill-climbing [16], input characteristic impedance (ICI) [17].…”

Section: Introductionmentioning

confidence: 99%

A new approach of variable step-size maximum power point tracking algorithm used in photovoltaic systems

Masmoudi¹,

Boulhares²,

Neçaïbia

2022

IJEECS

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To exploit the maximum power generated by the photovoltaic (PV) panel, it is necessary to use the maximum power point tracking (MPPT) controller. The main concern of MPPT algorithms is how to reach the maximum power point (MPP) quickly with less oscillation. To achieve this objective, this paper studies and compares the performance of different MPPT algorithms with a new proposed criterion of variable step size in terms of convergence speed towards the MPP and reduced oscillations around it. The proposed method utilizes a simple way to build multi-operating zones. In each field, the step size depends on the closeness to the MPP. The simulation results are obtained under Proteus and Arduino software; we use physical security information management (PSIM) software for the modeling of the PV panel and MATLAB software to display the comparative results between the different algorithms.

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An Improved Hill Climbing Search Algorithm for ROSA Coupling

Cited by 6 publications

References 0 publications

A review of metaheuristic optimization methods

A review of metaheuristic optimization methods

Automatic Segmentation of Natural Color Images in CIE Lab Space using Possibilistic Fuzzy C Means Clustering

A new approach of variable step-size maximum power point tracking algorithm used in photovoltaic systems

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