Designing Convolutional Neural Network Architecture by the Firefly Algorithm

Strumberger, Ivana; Tuba, Eva; Bačanin, Nebojša; Živković, Miodrag; Beko, Marko; Tuba, Milan

doi:10.1109/yef-ece.2019.8740818

Cited by 86 publications

(41 citation statements)

References 26 publications

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“…We note that we have been researching this domain for some time and that the research presented in this paper is the extension of our previous research [71,72].…”

Section: Research Objectives and Paper's Structurementioning

confidence: 88%

Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm Intelligence Metaheuristics

et al. 2020

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Computer vision is one of the most frontier technologies in computer science. It is used to build artificial systems to extract valuable information from images and has a broad range of applications in various areas such as agriculture, business, and healthcare. Convolutional neural networks represent the key algorithms in computer vision, and in recent years, they have attained notable advances in many real-world problems. The accuracy of the network for a particular task profoundly relies on the hyperparameters’ configuration. Obtaining the right set of hyperparameters is a time-consuming process and requires expertise. To approach this concern, we propose an automatic method for hyperparameters’ optimization and structure design by implementing enhanced metaheuristic algorithms. The aim of this paper is twofold. First, we propose enhanced versions of the tree growth and firefly algorithms that improve the original implementations. Second, we adopt the proposed enhanced algorithms for hyperparameters’ optimization. First, the modified metaheuristics are evaluated on standard unconstrained benchmark functions and compared to the original algorithms. Afterward, the improved algorithms are employed for the network design. The experiments are carried out on the famous image classification benchmark dataset, the MNIST dataset, and comparative analysis with other outstanding approaches that were tested on the same problem is conducted. The experimental results show that both proposed improved methods establish higher performance than the other existing techniques in terms of classification accuracy and the use of computational resources.

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“…We note that we have been researching this domain for some time and that the research presented in this paper is the extension of our previous research [71,72].…”

Section: Research Objectives and Paper's Structurementioning

confidence: 88%

Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm Intelligence Metaheuristics

et al. 2020

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“…Many modified and hybridized versions of the FA can be found in the literature [47][48][49][50]. Recently, the FA has been applied in the deep learning domain [51] for designing convolutional neural network (CNN) architecture [52]. The FA algorithm has also been implemented in many practical problems from the cloud computing domain, where it showed good performance.…”

Section: Swarm Intelligence Overview and Cloud Computing Applicationsmentioning

confidence: 99%

“…The FA is a well-known swarm optimizer that was devised by Yang in 2009 for tackling unconstrained optimization challenges [46]. By examining the literature, it can be seen that the FA is able to tackle many real-life NP hard problems [28,50,52,89,90].…”

Section: Fa's Search Equationmentioning

confidence: 99%

“…It should be noted that we took values for specific FA's search parameters as in [46]. According to the discussion presented in this paper, as well as regarding our previous experience with the FA metaheuristics [40,47,50,52], the best exploitation ability of the FA can be established by utilizing this set of parameters [46]. If other values would be taken, the performance of the FA search exploitation process would decrease significantly.…”

Section: The Woa-aefs Control Parameter Adjustmentsmentioning

confidence: 99%

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Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

et al. 2019

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The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its critical impact on the cloud system performance. Previously conducted research from this domain has shown that metaheuristics can substantially improve cloud system performance if they are used as scheduling algorithms. This paper introduces a hybridized whale optimization algorithm, that falls into the category of swarm intelligence metaheuristics, adapted for tackling the resource scheduling problem in cloud environments. To more precisely evaluate performance of the proposed approach, original whale optimization was also adapted for resource scheduling. Considering the two most important mechanisms of any swarm intelligence algorithm (exploitation and exploration), where the efficiency of a swarm algorithm depends heavily on their adjusted balance, the original whale optimization algorithm was enhanced by addressing its weaknesses of inappropriate exploitation-exploration trade-off adjustments and the premature convergence. The proposed hybrid algorithm was first tested on a standard set of bound-constrained benchmarks with the goal to more accurately evaluate its performance. After, simulations were performed using two different resource scheduling models in cloud computing with real, as well as with artificial data sets. Simulations were performed on the robust CloudSim platform. A hybrid whale optimization algorithm was compared with other state-of-the-art metaheurisitcs and heuristics, as well as with the original whale optimization for all conducted experiments. Achieved results in all simulations indicate that the proposed hybrid whale optimization algorithm, on average, outperforms the original version, as well as other heuristics and metaheuristics. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established, as well enhancements to the original whale optimization implementation.

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“…A firefly algorithm (FA), which was devised by Yang in 2009 [43], was inspired by the flashing behavior of the firefly insects. By examining available literature sources, it can be seen that the FA could be successfully adapted for different types of practical NP-hard challenges [6,[44][45][46][47]. This approach was also tested on standard benchmark problems in modified [48] and hybridized versions [49].…”

Section: Review Of Swarm Intelligence Metaheuristics and Its Applicatmentioning

confidence: 99%

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Strumberger

Tuba

Bačanin

et al. 2019

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Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end-users over high speed computer networks (the Internet). Cloudlet scheduling that has a significant impact on the overall cloud system performance represents one of the most important challenges in this domain. In this paper, we introduce implementations of the original and hybridized monarch butterfly optimization algorithm that belongs to the category of swarm intelligence metaheuristics, adapted for tackling the cloudlet scheduling problem. The hybridized monarch butterfly optimization approach, as well as adaptations of any monarch butterfly optimization version for the cloudlet scheduling problem, could not be found in the literature survey. Both algorithms were implemented within the environment of the CloudSim platform. The proposed hybridized version of the monarch butterfly optimization algorithm was first tested on standard benchmark functions and, after that, the simulations for the cloudlet scheduling problem were performed using artificial and real data sets. Based on the obtained simulation results and the comparative analysis with six other state-of-the-art metaheuristics and heuristics, under the same experimental conditions and tested on the same problem instances, a hybridized version of the monarch butterfly optimization algorithm proved its potential for tackling the cloudlet scheduling problem. It has been established that the proposed hybridized implementation is superior to the original one, and also that the task scheduling problem in cloud environments can be more efficiently solved by using such an algorithm with positive implications to the cloud management.

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Designing Convolutional Neural Network Architecture by the Firefly Algorithm

Cited by 86 publications

References 26 publications

Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm Intelligence Metaheuristics

Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm Intelligence Metaheuristics

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

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