Abstract:The research presented in this manuscript proposes a novel Harris Hawks optimization algorithm with practical application for evolving convolutional neural network architecture to classify various grades of brain tumor using magnetic resonance imaging. The proposed improved Harris Hawks optimization method, which belongs to the group of swarm intelligence metaheuristics, further improves the exploration and exploitation abilities of the basic algorithm by incorporating a chaotic population initialization and l… Show more
“…On the other hand, swarm intelligence algorithms are inspired by social behavior, collaboration, and communication among groups of relatively primitive individuals such as bees, ants, bats, fish, and fireflies. Swarm intelligence approaches were utilized with great success in solving a broad range of real-world problems in practice, including prolonging the network lifetime, clustering, and sensor localization in wireless sensor networks [ 4 , 48 , 50 ], cloud optimization problems [ 2 , 8 , 10 ], artificial neural networks optimization [ 7 , 20 , 27 , 36 ], machine learning-based COVID-19 cases prediction [ 49 , 52 ], and MRI classification optimization to name a few [ 5 , 9 ].…”
Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users—to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. Workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives—cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. Algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results’ quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches.
“…On the other hand, swarm intelligence algorithms are inspired by social behavior, collaboration, and communication among groups of relatively primitive individuals such as bees, ants, bats, fish, and fireflies. Swarm intelligence approaches were utilized with great success in solving a broad range of real-world problems in practice, including prolonging the network lifetime, clustering, and sensor localization in wireless sensor networks [ 4 , 48 , 50 ], cloud optimization problems [ 2 , 8 , 10 ], artificial neural networks optimization [ 7 , 20 , 27 , 36 ], machine learning-based COVID-19 cases prediction [ 49 , 52 ], and MRI classification optimization to name a few [ 5 , 9 ].…”
Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users—to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. Workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives—cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. Algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results’ quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches.
“…The proposed OBSCA-FS algorithm was tested for the dropout rate and validated against several cutting-edge metaheuristics approaches. The utilized CNN topology was derived from previous research published in 66 , that was dealing with the hyperparameters’ optimization for this particular dataset. The CNN structure shown in Fig.…”
“…6 obtained the best results in 66 , and it was consequently utilized in this research for testing and optimizing the dropout probability parameter. Finally, the CNN uses the Adam optimizer and the learning rate , again as the product of research 66 . Figure 6 The CNN structure utilized for MRI dataset.…”
Deep learning has recently been utilized with great success in a large number of diverse application domains, such as visual and face recognition, natural language processing, speech recognition, and handwriting identification. Convolutional neural networks, that belong to the deep learning models, are a subtype of artificial neural networks, which are inspired by the complex structure of the human brain and are often used for image classification tasks. One of the biggest challenges in all deep neural networks is the overfitting issue, which happens when the model performs well on the training data, but fails to make accurate predictions for the new data that is fed into the model. Several regularization methods have been introduced to prevent the overfitting problem. In the research presented in this manuscript, the overfitting challenge was tackled by selecting a proper value for the regularization parameter dropout by utilizing a swarm intelligence approach. Notwithstanding that the swarm algorithms have already been successfully applied to this domain, according to the available literature survey, their potential is still not fully investigated. Finding the optimal value of dropout is a challenging and time-consuming task if it is performed manually. Therefore, this research proposes an automated framework based on the hybridized sine cosine algorithm for tackling this major deep learning issue. The first experiment was conducted over four benchmark datasets: MNIST, CIFAR10, Semeion, and UPS, while the second experiment was performed on the brain tumor magnetic resonance imaging classification task. The obtained experimental results are compared to those generated by several similar approaches. The overall experimental results indicate that the proposed method outperforms other state-of-the-art methods included in the comparative analysis in terms of classification error and accuracy.
“…Furthermore, their full poten-tial is reached by incorporating hybridization techniques. The real world application of swarm intelligence solutions is vast from the clustering, node localization, and preserving of energy in wireless sensor networks [48][49][50][51], through to the scheduling problem with cloud tasks [2,52], the prediction of COVID-19 cases based on machine learning [53,54], MRI classification optimization [55,56], text document clustering [57], and the optimization of the artificial neural networks [58][59][60][61].…”
We live in a period when smart devices gather a large amount of data from a variety of sensors and it is often the case that decisions are taken based on them in a more or less autonomous manner. Still, many of the inputs do not prove to be essential in the decision-making process; hence, it is of utmost importance to find the means of eliminating the noise and concentrating on the most influential attributes. In this sense, we put forward a method based on the swarm intelligence paradigm for extracting the most important features from several datasets. The thematic of this paper is a novel implementation of an algorithm from the swarm intelligence branch of the machine learning domain for improving feature selection. The combination of machine learning with the metaheuristic approaches has recently created a new branch of artificial intelligence called learnheuristics. This approach benefits both from the capability of feature selection to find the solutions that most impact on accuracy and performance, as well as the well known characteristic of swarm intelligence algorithms to efficiently comb through a large search space of solutions. The latter is used as a wrapper method in feature selection and the improvements are significant. In this paper, a modified version of the salp swarm algorithm for feature selection is proposed. This solution is verified by 21 datasets with the classification model of K-nearest neighborhoods. Furthermore, the performance of the algorithm is compared to the best algorithms with the same test setup resulting in better number of features and classification accuracy for the proposed solution. Therefore, the proposed method tackles feature selection and demonstrates its success with many benchmark datasets.
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