Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems

Alzaqebah, Malek; Alrefai, Nashat; Ahmed, Eman A. E.; Jawarneh, Sana; Alsmadi, Mutasem K.

doi:10.11591/ijece.v10i4.pp3672-3684

Cited by 30 publications

(24 citation statements)

References 26 publications

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“…The significance of the obtained results can be determined through the Mann Whitney test as was demonstrated by McKnight and Najab [22]. 5 presents the Mann Whitney test's levels of marginal significance (p-values) based on the features number.…”

Section: Emperical Evaluation Results and Discussionmentioning

confidence: 99%

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Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Alzaqebah

Jawarneh

Mohammad

et al. 2021

IJECE

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Machine learning has been expansively examined with data classification as the most popularly researched subject. The accurateness of prediction is impacted by the data provided to the classification algorithm. Meanwhile, utilizing a large amount of data may incur costs especially in data collection and preprocessing. Studies on feature selection were mainly to establish techniques that can decrease the number of utilized features (attributes) in classification, also using data that generate accurate prediction is important. Hence, a particle swarm optimization (PSO) algorithm is suggested in the current article for selecting the ideal set of features. PSO algorithm showed to be superior in different domains in exploring the search space and local search algorithms are good in exploiting the search regions. Thus, we propose the hybridized PSO algorithm with an adaptive local search technique which works based on the current PSO search state and used for accepting the candidate solution. Having this combination balances the local intensification as well as the global diversification of the searching process. Hence, the suggested algorithm surpasses the original PSO algorithm and other comparable approaches, in terms of performance.

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Section: Emperical Evaluation Results and Discussionmentioning

confidence: 99%

“…bees, ants, birds, and moths [13][14][15][16][17]. Techniques based on swarm intelligence have been widely used as a wrapper method for feature selection [18], for instance, bees algorithm [19], ant colony optimization (ACO) [20], butterfly optimization algorithm [21] and moth optimization algorithm [22].…”

Section: Introductionmentioning

confidence: 99%

Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Alzaqebah

Jawarneh

Mohammad

et al. 2021

IJECE

Self Cite

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“…The Moth-Flame Optimisation (MFO) algorithm [23] is a state-of-the-art, natureinspired meta-heuristic method that demonstrates a considerable performance in optimising various numerical [42,43] and real engineering [44,45] optimisation problems. The principal motivation of the Moth-Flame Optimisation algorithm is the navigating strategy of the moth, called 'transverse orientation'.…”

Section: Improved Moth-flame Optimisation (Imfo)mentioning

confidence: 99%

Multi-Mode Wave Energy Converter Design Optimisation Using an Improved Moth Flame Optimisation Algorithm

et al. 2021

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Ocean renewable wave power is one of the more encouraging inexhaustible energy sources, with the potential to be exploited for nearly 337 GW worldwide. However, compared with other sources of renewables, wave energy technologies have not been fully developed, and the produced energy price is not as competitive as that of wind or solar renewable technologies. In order to commercialise ocean wave technologies, a wide range of optimisation methodologies have been proposed in the last decade. However, evaluations and comparisons of the performance of state-of-the-art bio-inspired optimisation algorithms have not been contemplated for wave energy converters’ optimisation. In this work, we conduct a comprehensive investigation, evaluation and comparison of the optimisation of the geometry, tether angles and power take-off (PTO) settings of a wave energy converter (WEC) using bio-inspired swarm-evolutionary optimisation algorithms based on a sample wave regime at a site in the Mediterranean Sea, in the west of Sicily, Italy. An improved version of a recent optimisation algorithm, called the Moth–Flame Optimiser (MFO), is also proposed for this application area. The results demonstrated that the proposed MFO can outperform other optimisation methods in maximising the total power harnessed from a WEC.

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“…In wrapper method, a system is trained on a group of attributes. 16 This method improves the set of attributes based on the performance of the classifier and yields better results even though it is found to be time-consuming. Embedded method takes advantage of both filter and wrapper methods.…”

Section: Feature Engineeringmentioning

confidence: 99%

Enhanced texture classification through feature compaction using dihybrid bio‐inspired computation techniques

Bagavathi¹,

Saraniya²

2021

Concurrency and Computation

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Synchronous data processing is often preferred considering the volume of data available and the requirement of processed data. Data volume increase from current technical advancements should be balanced and suitably dealt with efficient feature processing techniques. Feature selection can efficiently improve the concurrency in data processing on a completely uncorrelated and separately processable feature set.Texture analysis is a significant application of image processing that analyses specific patterns in an image. As the volume of image data available for producing better classification models progressively moves to be out of the human-manageable range, it is the automation methods that aid the researchers to manage the data. When the number of samples in the data and the dimensionality of the problem is too high, the purpose of data analysis is often compromised. Feature engineering can be utilized to improve the efficiency of the model with a smaller subset of features. This work proposes dibyhrid bio-inspired computation based feature compaction, that is a combination of improvised genetic algorithm and selective tabu search for texture classification problem.Machine learning techniques are employed to evaluate and minimize the validation error from the applied computational methods.

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Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems

Cited by 30 publications

References 26 publications

Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Multi-Mode Wave Energy Converter Design Optimisation Using an Improved Moth Flame Optimisation Algorithm

Enhanced texture classification through feature compaction using dihybrid bio‐inspired computation techniques

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