A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems

Tian, Ai-Qing; Chu, Shu-Chuan; Pan, Jeng‐Shyang; Liang, Yongquan

doi:10.3390/pr8030356

Cited by 26 publications

(10 citation statements)

References 47 publications

(62 reference statements)

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“…The algorithm improved the convergence speed and also enhanced the superiority of global search in diverse use-cases. A hybrid algorithm that is fast, stable, and able to universally optimize the maximum power point tracking algorithm was presented by [135]. The algorithm is a composition of a new pigeon population algorithm called parallel and compact pigeon-inspired optimization (PCPIO) with maximum power point tracking (MPPT), which can address the problem MPPT cannot reach the near-global maximum power point.…”

Section: Trajectory-based Algorithms (Tbas)mentioning

confidence: 99%

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Omolara

Abiodun

Alawida

et al. 2021

Neural Comput & Applic

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Specialized data preparation techniques, ranging from data cleaning, outlier detection, missing value imputation, feature selection (FS), amongst others, are procedures required to get the most out of data and, consequently, get the optimal performance of predictive models for classification tasks. FS is a vital and indispensable technique that enables the model to perform faster, eliminate noisy data, remove redundancy, reduce overfitting, improve precision and increase generalization on testing data. While conventional FS techniques have been leveraged for classification tasks in the past few decades, they fail to optimally reduce the high dimensionality of the feature space of texts, thus breeding inefficient predictive models. Emerging technologies such as the metaheuristics and hyper-heuristics optimization methods provide a new paradigm for FS due to their efficiency in improving the accuracy of classification, computational demands, storage, as well as functioning seamlessly in solving complex optimization problems with less time. However, little details are known on best practices for case-to-case usage of emerging FS methods. The literature continues to be engulfed with clear and unclear findings in leveraging effective methods, which, if not performed accurately, alters precision, real-world-use feasibility, and the predictive model's overall performance. This paper reviews the present state of FS with respect to metaheuristics and hyper-heuristic methods. Through a systematic literature review of over 200 articles, we set out the most recent findings and trends to enlighten analysts, practitioners and researchers in the field of data analytics seeking clarity in understanding and implementing effective FS optimization methods for improved text classification tasks.

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Section: Trajectory-based Algorithms (Tbas)mentioning

confidence: 99%

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Omolara

Abiodun

Alawida

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

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“…The P&O algorithm [16,36] is widely used by researchers due to its low cost and easy implementation. This algorithm is based on the PV-M P pv-m -V pv-m characteristic curve.…”

Section: Perturbation and Observation Algorithmmentioning

confidence: 99%

Novel Global-MPPT Control Strategy Considering the Variation in the Photovoltaic Module Output Power and Loads for Solar Power Systems

Lin

Huang

et al. 2022

Processes

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This research proposed a novel global maximum power point tracking (global-MPPT) algorithm. The proposed algorithm eliminates the perturbation and observation (P&O) technique disturbance problem that the power point will be stuck at the local peak power point under a partial shading condition (PSC). The proposed global-MPPT algorithm detects the photovoltaic module (PV-M) environment irradiance level by the relationship between the output power and voltage of the PV-M. In the proposed algorithm, the important parameter w is determined by the PV-M output power and irradiance level, which is also the compensation parameter that corresponds to the relationship of temperature. The proposed global-MPPT algorithm is aimed to predict the best duty cycle of the global-MPPT based on the irradiance level, parameter w, PV-M output voltage, and load, and then achieve the maximum power point (MPP) quickly and accurately. The measurement results under UIC and PSC verify that the proposed global-MPPT technique performs better than the particle swarm optimization (PSO) and P&O techniques. This research contributes to the proposed method that can find the global-MPP in time based on the irradiance level, temperature, and load.

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“…Nature-inspired optimization algorithms have two phases: exploration and exploitation. Exploration is a large-scale search to prevent falling into a local optimum, while the exploitation phase is a small-scale search to find the optimal solution [ 41 , 42 ]. Grasshoppers can instinctively perform these two steps to find the target.…”

Section: Preliminariesmentioning

confidence: 99%

Improved Binary Grasshopper Optimization Algorithm for Feature Selection Problem

Wang

Chu

Tian

et al. 2022

Entropy

Self Cite

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The migration and predation of grasshoppers inspire the grasshopper optimization algorithm (GOA). It can be applied to practical problems. The binary grasshopper optimization algorithm (BGOA) is used for binary problems. To improve the algorithm’s exploration capability and the solution’s quality, this paper modifies the step size in BGOA. The step size is expanded and three new transfer functions are proposed based on the improvement. To demonstrate the availability of the algorithm, a comparative experiment with BGOA, particle swarm optimization (PSO), and binary gray wolf optimizer (BGWO) is conducted. The improved algorithm is tested on 23 benchmark test functions. Wilcoxon rank-sum and Friedman tests are used to verify the algorithm’s validity. The results indicate that the optimized algorithm is significantly more excellent than others in most functions. In the aspect of the application, this paper selects 23 datasets of UCI for feature selection implementation. The improved algorithm yields higher accuracy and fewer features.

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A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems

Cited by 26 publications

References 47 publications

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Novel Global-MPPT Control Strategy Considering the Variation in the Photovoltaic Module Output Power and Loads for Solar Power Systems

Improved Binary Grasshopper Optimization Algorithm for Feature Selection Problem

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