Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer

Hassanin, Mohamed F.; Shoeb, Abdullah M.; Hassanien, Aboul Ella

doi:10.4018/978-1-5225-2229-4.ch048

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Comparative analysis of the results showed that MVO outperformed PSO, GA, DE, firefly, and CS optimization algorithms. In related work, Hassanin and colleagues 79 discussed how to optimize multilayer feedforward neural networks using MVO. Another interesting work was presented by Hossam and colleagues, 80 who proposed the use of MVO for performance tuning of the parameters of SVM while simultaneously selecting optimal features.…”

Section: Metaheuristic Algorithms and Related Workmentioning

confidence: 99%

Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

Oyelade

Ezugwu

2021

Concurrency and Computation

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Convolutional neural networks (CNN) are deep learning models widely reported as performing well in several image classification tasks. Training the networks for optimal performance is considered an NP-hard problem. The combination of a metaheuristic algorithm with CNN has been proposed to address this problem because the metaheuristic algorithms have efficient performance, parameter tuning, and stagnation prevention methods. Moreover, there is now an increasing need to apply the best performing metaheuristic algorithm to optimize the parameters, training and learning rate of the CNN models. This study aims to investigate the best performing metaheuristic algorithm for fine-tuning the weights, biases, and hyperparameters of CNN networks for solving the problem of characterization of abnormalities in breast images. Furthermore, hybrid models consisting of a CNN architecture and five representative metaheuristic algorithms to efficiently detect breast cancer abnormalities are presented. The adopted approach involves training a CNN network using genetic algorithm (GA), whale optimization algorithm (WOA), multiverse optimizer (MVO), satin bower optimization (SBO), and life choice-based optimization (LCBO) algorithms to optimize only weights and bias of the model. Two categories of experiments were carried out: the first involved training of the CNN model, while the second involved optimizing the training of CNN model with metaheuristic algorithms. A comparative analysis of the impact of the proposed GA, WOA, MVO, SBO, and LCBO metaheuristic algorithms on the performance of the CNN architecture was carried out. Empirical and statistical analyses are presented to validate further the findings obtained in the study. Results obtained showed that MVO, SBO and LCBO outperformed GA, WOA. The classification accuracy obtained for CNN-GA, CNN-WOA, CNN-MVO, CNN-SBO, and CNN-LCBO at the fifth epoch was 0.76, 0.75, 0.84, 0.80, and 0.86, respectively. At the same time, the traditional CNN achieved an accuracy of 0.66. The outcome of this study showed that contrary to widespread practice, physics-based, biology-based, and human-based optimization algorithms promise better performance parameter tuning for CNN as compared with evolutionary-based and swarm-based metaheuristic algorithms.

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Section: Metaheuristic Algorithms and Related Workmentioning

confidence: 99%

Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

Oyelade

Ezugwu

2021

Concurrency and Computation

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“…The multiverse optimizer (MVO) algorithm has also been widely used in different fields. In References [40,41], MVOs were used to train multilayer perceptron neural networks. Benmessahel [42] combined the MVO and the artificial neutral network to develop an intrusion detection system.…”

Section: Introductionmentioning

confidence: 99%

Novel Swarm Intelligence Algorithm for Global Optimization and Multi-UAVs Cooperative Path Planning: Anas Platyrhynchos Optimizer

et al. 2020

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In this study, a novel type of swarm intelligence algorithm referred as the anas platyrhynchos optimizer is proposed by simulating the cluster action of the anas platyrhynchos. Starting from the core of swarm intelligence algorithm, on the premise of the use of few parameters and ease in implementation, the mathematical model and algorithm flow of the anas platyrhynchos optimizer are given, and the balance between global search and local development in the algorithm is ensured. The algorithm was applied to a benchmark function and a cooperative path planning solution for multi-UAVs as a means of testing the performance of the algorithm. The optimization results showed that the anas platyrhynchos optimizer is more superior in solving optimization problems compared with the mainstream intelligent algorithm. This study provides a new idea for solving more engineering problems.

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Multi-verse Optimizer: Theory, Literature Review, and Application in Data Clustering

Aljarah

Mafarja

Heidari

et al. 2019

Studies in Computational Intelligence

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Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer

Cited by 5 publications

References 14 publications

Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

Novel Swarm Intelligence Algorithm for Global Optimization and Multi-UAVs Cooperative Path Planning: Anas Platyrhynchos Optimizer

Multi-verse Optimizer: Theory, Literature Review, and Application in Data Clustering

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