Solving Multi-Objective Problems Using Bird Swarm Algorithm

Houssein, Essam H.; Ahmed, Mohammed M.; Elaziz, Mohamed Abd; Ewees, Ahmed A.; Ghoniem, Rania M.

doi:10.1109/access.2021.3063218

Cited by 12 publications

(5 citation statements)

References 65 publications

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“…Meta-heuristics methods have recently demonstrated excellent performance in dynamic and global search optimizations [12]; the methods have been widely applied in a variety of fields [13], including global optimization [14], image processing [15], and cloud computing scheduling [16].…”

Section: Related Workmentioning

confidence: 99%

The Effect of Meta-heuristic Methods on the Performance of ImageClassification

Anai,

Mersal,

Mostafa

2024

Iraqi Journal of Science

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Classification accuracy is strongly affected by the quality of the input features. In recent years, datasets have increased in size and number of features. Analysis of huge datasets can be challenging due to redundant, noisy, and irrelevant features that mayreduce the classifier's performance. Feature selection is a vital process in which the best subset of features from the original dataset is chosen. The feature selection strategy is critical for increasing classification accuracy while decreasing computational costs. This research proposed a method for classifying lip print images by exploiting meta-heuristic methods and optimization-based feature selection methods. It involves four main phases: pre-processing, feature extraction, feature selection, and classification. After pre-processing, the features are extracted from the enhanced image. Meta-heuristic methods such as Genetic Algorithm (GA), ParticleSwarm Optimization (PSO), and Water Cycle Algorithm (WCA) are studied for feature selection using the mean function as the objective function. Finally, the lip print images are classified using a support vector machine (SVM). In this research, the experimental results are compared in terms of accuracy, error, sensitivity, and precision rate between three meta-heuristic methods and the accuracy rate of the proposed method with other algorithms that do not use meta-heuristic methods. The accuracy reached 97.9%, 96.8%, and 95% using WCA, PSO, and GA, respectively.

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Section: Related Workmentioning

confidence: 99%

The Effect of Meta-heuristic Methods on the Performance of ImageClassification

Anai,

Mersal,

Mostafa

2024

Iraqi Journal of Science

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“…There exist multiple intelligent algorithms such as the bat algorithm, artificial bee colony algorithm, wolf optimization algorithm, and bird swarm algorithm [30][31][32][33]. The results of using different intelligent algorithms to solve optimization problems in different fields also differ.…”

Section: The Whale Optimization Algorithm (Woa)mentioning

confidence: 99%

A Coal Seam Thickness Prediction Model Based on CPSAC and WOA–LS-SVM: A Case Study on the ZJ Mine in the Huainan Coalfield

et al. 2022

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The precise prediction of coal seam thickness in operating mines is crucial for the construction of transparent mines. Geological borehole data or a small amount of seismic information is frequently used in traditional coal seam thickness prediction methods; however, these methods have poor precision. In this study, we introduced a model for predicting coal seam thickness based on the comprehensive preference for seismic attribute combination (CPSAC) and the least squares support vector machine (LS-SVM) optimized by the whale optimization algorithm (WOA). We used the CPSAC to modify the mass disturbed data in the seismic attribute data to predict the coal seam thickness. To achieve this the sample size was reduced by optimizing the seismic attribute combinations, and the modified attribute data was entered into the LS-SVM., Furthermore, to create an accurate prediction model for coal thickness, we employed the WOA to determine the optimal penalty coefficient and kernel coefficient of the LS-SVM. An empirical case study was conducted in the northeast mining area of the ZJ mine in the Huainan coalfield. The coal thickness of two mining faces in this research area were estimated and compared, demonstrating the proposed method’s high prediction accuracy. The proposed method has guiding implications for developing an accurate mining geological model and facilitating the accurate use of coal resources.

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“…Various bioinspired algorithms, including swarm intelligence and evolutionary algorithms, have been used in various optimization problems involving engineering problems (Houssein et al , 2021; Premkumar et al , 2021) and electromagnetic devices (Ayala et al , 2016; Di Barba, 2010; Di Barba et al , 2018, 2022; Kumar et al , 2022).…”

Section: Introductionmentioning

confidence: 99%

Crowding-based multi-objective artificial gorilla troops optimizer for brushless direct current motor design optimization

Bensoltane,

Belli

2023

COMPEL

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Purpose This paper aims to present a novel multi-objective version of the Gorilla Troops optimizer (GTO), based on crowding distance, to achieve the optimal design of a brushless direct current motor. Design/methodology/approach In the proposed algorithm, the crowding distance technique was integrated into the GTO to perform the leader selection and also for the external archive refinement from extra non-dominated solutions. Furthermore, with a view to improving the diversity of non-dominated solutions in the external archive, mutation operator was used. For constrained problems, an efficient strategy was adopted. The proposed algorithm is referred to as CD-MOGTO. Findings To validate the effectiveness of the proposed approach, it was initially tested on three constrained multi-objective problems; thereafter, it was applied to optimize the design variables of brushless direct current motor to concurrently fulfill six inequality constraints, maximize efficiency and minimize total mass. Originality/value The results revealed the high potential of the proposed algorithm over different recognized algorithms in solving constrained multi-objective issues and the brushless direct current motors.

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Solving Multi-Objective Problems Using Bird Swarm Algorithm

Cited by 12 publications

References 65 publications

The Effect of Meta-heuristic Methods on the Performance of ImageClassification

The Effect of Meta-heuristic Methods on the Performance of ImageClassification

A Coal Seam Thickness Prediction Model Based on CPSAC and WOA–LS-SVM: A Case Study on the ZJ Mine in the Huainan Coalfield

Crowding-based multi-objective artificial gorilla troops optimizer for brushless direct current motor design optimization

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