ArticleSoft computing2023
An improved butterfly optimization algorithm for training the feed-forward artificial neural networks.
Article in Soft computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
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- MLP Enhanced COScientific reports · 2025Article
- A comparative study on bath and horn ultrasound-assisted modification of bentonite and their effects on the bleaching efficiency of soybean and sunflower oil: Machine learning as a new approach for mathematical modeling.Food science & nutrition · 2024Article
- Differential Mutation Incorporated Quantum Honey Badger Algorithm with Dynamic Opposite Learning and Laplace Crossover for Fuzzy Front-End Product Design.Biomimetics (Basel, Switzerland) · 2024Article
- Sepsis Prediction by Using a Hybrid Metaheuristic Algorithm: A Novel Approach for Optimizing Deep Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- An improved butterfly optimization algorithm for training the feed-forward artificial neural networks.Soft computing · 2023Article
- Archive-based coronavirus herd immunity algorithm for optimizing weights in neural networks.Neural computing & applications · 2023Article
- A modified weighted chimp optimization algorithm for training feed-forward neural network.PloS one · 2023Article
- Artificial Neural Networks for the Prediction of Monkeypox Outbreak.Tropical medicine and infectious disease · 2022Article
- Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review.Neural processing letters · 2022Review
- Parameter-Adaptive TVF-EMD Feature Extraction Method Based on Improved GOA.Sensors (Basel, Switzerland) · 2022Article
- A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data.Computational intelligence and neuroscience · 2022Article
- A hybrid ensemble approach for diabetes prediction using consensus-based feature selection.Digital healthArticle
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3 authors.
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Abstract
Artificial neural network (ANN) which is an information processing technique developed by modeling the nervous system of the human brain is one of the most powerful learning methods today. One of the factors that make ANN successful is its training algorithm. In this paper, an improved butterfly optimization algorithm (IBOA) based on the butterfly optimization algorithm was proposed for training the feed-forward artificial neural networks. The IBOA algorithm has the chaotic property which helps optimization algorithms to explore the search space more dynamically and globally. In the experiments, ten chaotic maps were used. The success of the IBOA algorithm was tested on 13 benchmark functions which are well known to those working on global optimization and are frequently used for testing and analysis of optimization algorithms. The Tent-mapped IBOA algorithm outperformed the other algorithms in most of the benchmark functions. Moreover, the success of the IBOA-MLP algorithm also has been tested on five classification datasets (xor, balloon, iris, breast cancer, and heart) and the IBOA-MLP algorithm was compared with four algorithms in the literature. According to the statistical performance metrics (sensitivity, specificity, precision,
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