Using Metaheuristics in Discrete-Event Simulation

França, Reinaldo Padilha; Monteiro, Ana Carolina Borges; Estrela, Vania V.; Razmjooy, Navid

doi:10.1007/978-3-030-56689-0_14

Cited by 4 publications

(4 citation statements)

References 45 publications

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“…At the first step, an efficient optimizer approach needs to explore the search space to discover different solutions. After an adequate transition, the technique often employs local details for generating some improved solutions, which are commonly in the areas close to the existing solutions [53,54].…”

Section: Chimp Optimization Algorithmmentioning

confidence: 99%

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Chimp Optimization Algorithm to Optimize a Convolutional Neural Network for Recognizing Persian/Arabic Handwritten Words

Khosravi

Chalechale

2022

Mathematical Problems in Engineering

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Handwritten character recognition is an attractive subject in computer vision. In recent years, numerous researchers have implemented techniques to recognize handwritten characters using optical character recognition (OCR) approaches for many languages. One the most common methods to improve the OCR accuracy is based on convolutional neural networks (CNNs). A CNN model contains several kernels accompanying with pooling layers and nonlinear functions. This model overcomes the problem of adjusting the value of weights and interconnections of the neural network (NN) for creating an appropriate pipeline to process the spatial and temporal information. However, the training process of a CNN is a challenging issue. Various optimization strategies have been recently utilized for optimizing CNN’s biases and weights such as firefly algorithm (FA) and ant colony optimization (ACO) algorithms. In this study, we apply a well-known nature-inspired technique called chimp optimization algorithm (ChOA) to train a classical CNN structure LeNet-5 for Persian/Arabic handwritten recognition. The proposed method is tested on two known and publicly available handwritten word datasets. To deeply investigate and evaluate the approach, the results are compared with three optimization methods including ACO, FA, and particle swarm optimization (PSO). Outcomes indicated that the proposed ChOA technique considerably improves the performance of the original LeNet model and also shows a better performance than the others.

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Section: Chimp Optimization Algorithmmentioning

confidence: 99%

“…is unconditional behavior in the ultimate phase leads to convergence rate progress and exploitation step [53,54].…”

Section: Chimp Optimization Algorithmmentioning

confidence: 99%

Chimp Optimization Algorithm to Optimize a Convolutional Neural Network for Recognizing Persian/Arabic Handwritten Words

Khosravi

Chalechale

2022

Mathematical Problems in Engineering

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“…However, these methods can be time-consuming and invasive that can have restrictions with regard to effectiveness and the precision. Therefore, oral cancer diagnosis requires efficient and meticulous strategies [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of Northern Goshawk optimization algorithm

Zhang,

Shi,

Youssefi

2024

Heliyon

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“…Metaheuristic algorithms can be categorized into the following subgroups [ 37 – 39 ]: Swarm based methods act based on animal social behavior like PSO [ 40 , 41 ], WOA [ 42 ] Evolutionary methods act based on a natural evolutionary process like Genetic algorithm (GA) [ 43 ] Biological based optimizers like Satin Bowerbird Optimizer (SBO) [ 44 ] Human-based algorithms inspired from human behavior such as Life Choice Based Optimizer [ 45 ] System-based algorithms inspired by natural ecosystems such as Artificial Ecosystem-based Optimizer (AEO) [ 46 ] Physics-based methods that mimic the physical phenomenon in nature like Equilibrium optimizer [ 47 ] and Simulated Annealing [ 48 ] …”

Section: Introductionmentioning

confidence: 99%

Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network

Saadi

Sarshar

Sadeghi

et al. 2022

Journal of Healthcare Engineering

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One of the leading algorithms and architectures in deep learning is Convolution Neural Network (CNN). It represents a unique method for image processing, object detection, and classification. CNN has shown to be an efficient approach in the machine learning and computer vision fields. CNN is composed of several filters accompanied by nonlinear functions and pooling layers. It enforces limitations on the weights and interconnections of the neural network to create a good structure for processing spatial and temporal distributed data. A CNN can restrain the numbering of free parameters of the network through its weight-sharing property. However, the training of CNNs is a challenging approach. Some optimization techniques have been recently employed to optimize CNN’s weight and biases such as Ant Colony Optimization, Genetic, Harmony Search, and Simulated Annealing. This paper employs the well-known nature-inspired algorithm called Shuffled Frog-Leaping Algorithm (SFLA) for training a classical CNN structure (LeNet-5), which has not been experienced before. The training method is investigated by employing four different datasets. To verify the study, the results are compared with some of the most famous evolutionary trainers: Whale Optimization Algorithm (WO), Bacteria Swarm Foraging Optimization (BFSO), and Ant Colony Optimization (ACO). The outcomes demonstrate that the SFL technique considerably improves the performance of the original LeNet-5 although using this algorithm slightly increases the training computation time. The results also demonstrate that the suggested algorithm presents high accuracy in classification and approximation in its mechanism.

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Using Metaheuristics in Discrete-Event Simulation

Cited by 4 publications

References 45 publications

Chimp Optimization Algorithm to Optimize a Convolutional Neural Network for Recognizing Persian/Arabic Handwritten Words

Chimp Optimization Algorithm to Optimize a Convolutional Neural Network for Recognizing Persian/Arabic Handwritten Words

Oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of Northern Goshawk optimization algorithm

Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network

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