Feature selection via Lèvy Antlion optimization

This paper seeks to improve the photovoltaic (PV) system efficiency using metaheuristic, optimized fractional order incremental conductance (FO-INC) control. The proposed FO-INC controls the output voltage of the PV arrays to obtain maximum power point tracking (MPPT). Due to its simplicity and efficiency, the incremental conductance MPPT (INC-MPPT) is one of the most popular algorithms used in the PV scheme. However, owing to the nonlinearity and fractional order (FO) nature of both PV and DC-DC converters, the conventional INC algorithm provides a trade-off between monitoring velocity and tracking precision. Fractional calculus is used to provide an enhanced dynamical model of the PV system to describe nonlinear characteristics. Moreover, three metaheuristic optimization techniques are applied; Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and AntLion Optimizer (ALO) are used for tuning the FO parameters of the proposed INC-MPPT. A MATLAB-Simulink-based model of the PV and optimization have been developed and simulated for different INC-MPPT techniques. Different techniques aim to control the boost DC-DC converter towards the MPP. The proposed optimization algorithms are, also, developed and implemented in MATLAB to tune the target parameters. Four performance indices are also introduced in this research to show the reliability of the comparative analysis of the proposed FO-INC with metaheuristic optimization and the conventional INC-MPPT algorithms when applied to a dynamical PV system under rapidly changing weather conditions. The simulation results show the effective performance of the proposed metaheuristic optimized FO-INC as a MPPT control for different climatic conditions with disturbance rejection and robustness analysis.

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“…ALO is suggested based on the Emary and Zawbaa [24] mathematical model. e ALO algorithm simulates the interaction between the traps.…”

Section: Antlion Optimizer Optimization (Alo)mentioning

confidence: 99%

Metaheuristic Optimization of Fractional Order Incremental Conductance (FO‐INC) Maximum Power Point Tracking (MPPT)

et al. 2019

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“…But over the 20 runs, the average performance of the GWO algorithm was the most accurate in predicting porosity and the average performance of the SSO algorithm achieved the most accurate prediction for the tensile strength. The different performances of the various bio-inspired optimization algorithms are primarily due to the intrisic nature in modelling, as detailed in the recent publications [45], [46], [47] [48], [49], [50], [51], [52], [53], [54]. Figure 12).…”

Section: Model Predictionmentioning

confidence: 99%

Computational intelligence modelling of pharmaceutical tabletting processes using bio-inspired optimization algorithms

Zawbaa

Schiano

Pérez-Gandarillas

et al. 2018

Advanced Powder Technology

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In pharmaceutical development, it is very useful to exploit the knowledge of the causal relationship between product quality and critical material attributes (CMA) in developing new formulations and products, and optimizing manufacturing processes. With the big data captured in the pharmaceutical industry, computational intelligence (CI) models could potentially be used to identify critical quality attributes (CQA), CMA and critical process parameters (CPP). The objective of this study was to develop computational intelligence models for pharmaceutical tabletting processes, for which bioinspired feature selection algorithms were developed and implemented for optimisation while artificial neural network (ANN) was employed to pre

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“…Recently, some hybrid bioinspired heuristic approaches were proposed to reduce the feature size of the input data such as the work of Zawbaa et al [23], whereas a hybrid algorithm is proposed to handle the large-dimensionality small-instance set feature selection problems. In [24], another algorithm is proposed to handle the feature selection problem using Levy Antlion optimization. e flower pollination algorithm [25] is used also in another research to make an attribute reduction after modifying it using new adaptive techniques to handle such problems.…”

Section: Related Workmentioning

confidence: 99%

Feature Reduction Based on Hybrid Efficient Weighted Gene Genetic Algorithms with Artificial Neural Network for Machine Learning Problems in the Big Data

Mohammed

Alhayali

Bayat

et al. 2018

Scientific Programming

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A large amount of data being generated from different sources and the analyzing and extracting of useful information from these data becomes a very complex task. The difficulty of dealing with big data arises from many factors such as the high number of features, existence of lost data, and variety of data. One of the most effective solutions that used to overcome the huge amount of big data is the feature reduction process. In this paper, a set of hybrid and efficient algorithms are proposed to classify the datasets that have large feature size by merging the genetic algorithms with the artificial neural networks. The genetic algorithms are used as a prestep to significantly reduce the feature size of the analyzed data before handling that data using machine learning techniques. Reducing the number of features simplifies the task of classifying the analyzed data and enhances the performance of the machine learning algorithms that are used to extract valuable information from big data. The proposed algorithms use a new gene-weight mechanism that can significantly enhance the performance and decrease the required search time. The proposed algorithms are applied on different datasets to pick the most relative and important features before applying the artificial neural networks algorithm, and the results show that our proposed algorithms can effectively enhance the classifying performance over the tested datasets.

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Feature selection via Lèvy Antlion optimization

Cited by 67 publications

References 45 publications

Metaheuristic Optimization of Fractional Order Incremental Conductance (FO‐INC) Maximum Power Point Tracking (MPPT)

Metaheuristic Optimization of Fractional Order Incremental Conductance (FO‐INC) Maximum Power Point Tracking (MPPT)

Computational intelligence modelling of pharmaceutical tabletting processes using bio-inspired optimization algorithms

Feature Reduction Based on Hybrid Efficient Weighted Gene Genetic Algorithms with Artificial Neural Network for Machine Learning Problems in the Big Data

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