Multi-moth flame optimization for solving the link prediction problem in complex networks

Barham, Reham; Sharieh, Ahmad; Sleit, Azzam

doi:10.1007/s12065-019-00257-y

Cited by 15 publications

(7 citation statements)

References 39 publications

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“…The values of moths according to their fitness function in current iteration (ie, OM itr ) and previous iteration (ie, OM itr-1 ) are compared. If the current values of moths fitness function are better than flames (ie, the fitness function moths in previous iteration (ie, OM itr-1 )) would be replaced with them (ie, OF=Sort (OM itr-1 , OM itr )) (lines [21][22].…”

Section: Proposed Algorithmmentioning

confidence: 99%

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Ghobaei‐Arani

Souri

Safara

et al. 2019

Trans Emerging Tel Tech

145

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With the rapid development of in modern technologies has led to a novel generation of cyberspace equipment and secure analysis methods such as cyber‐physical system (CPS). The CPS system refers to an intelligent dependable system of embedded smart devices used to monitor and control the cyberspace conditions in a fog computing environment. The fog computing environment connects resources even human beings together to enhance the quality of life through performing of CPS applications using computation resources at the network edge. The optimization of task scheduling for the CSP applications as a challenging issue has been considered as the NP‐hard problem in fog computing environment which is not trivial. On the one hand, reasonable tasks scheduling can increase resource utilization, which can avoid less idle resource. This paper presents a task scheduling algorithm based on moth‐flame optimization algorithm to assign an optimal set of tasks to fog nodes to meet the satisfaction of quality of service requirements of CPS applications in such a way that the total execution time of tasks is minimized. The minimization of task execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed solution is carried out in iFogSim toolkit. As a result of the simulation based on proposed algorithm found, the optimal solution for the scheduling of tasks and equal distribution of tasks to fog nodes has been provided, and less total execution time consumption has been achieved compared with other algorithms.

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Section: Proposed Algorithmmentioning

confidence: 99%

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Ghobaei‐Arani

Souri

Safara

et al. 2019

Trans Emerging Tel Tech

145

View full text Add to dashboard Cite

show abstract

“…Conversely, if the stop criterion is not met, an incorrect item will be returned. As well as, the main method of updating the butterfly search space is the logarithmic helix [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Computational Modeling of Desiccation Properties (CW, LS, and VS) of Waste-Based Activated Ash-Treated Black Cotton Soil for Sustainable Subgrade Using Artificial Neural Network, Gray-Wolf, and Moth-Flame Optimization Techniques

Onyelowe

Shakeri

Amini-Khoshalan

et al. 2022

Advances in Materials Science and Engineering

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Artificial neural network (ANN), gray-wolf, and moth-flame optimization (GWO and MFO) techniques have been used in this research work to predict the effect of activated sawdust ash (ASDA) on the crack width (CW), linear shrinkage (LS), and volumetric shrinkage (VS) of a black cotton soil utilized as a subgrade material. Problematic soils or black cotton soils are not good pavement foundation materials except that they are pretreated in order to meet the basic strength characteristics required for roads in Nigeria. Due to this reason, there has been ongoing research to evaluate the best practices in which black cotton soils can be favorably utilized in earthwork construction. On the other hand, there is a huge concern on the solid waste management system in the wood processing environment and the recycling of sawdust into ash and its reuse as an alternative binder has offered a sustainable disposal system. The work tries to use AI-based techniques to predict the crack and shrinkage behaviors of BCS treated with saw dust ash activated with alkali materials. There was appreciable improvement in the shrinkage and crack parameters over the 30-day drying period due to the addition of ASDA. The intelligent model results showed that the three techniques successfully predicted the CW, LS, and VS with a performance accuracy above 90%, while ANN produced the minimal error in performance outperforming the other techniques. Sensitivity study showed that the drying time (T) was the most influential of the studied parameter. Hence, soil stabilization has shown its potential system of waste management in the wood processing industry.

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“…Moth-Flame Optimization (MFO) [16] is a new swarm intelligence bionic algorithm that taken the inspiration from natural moth behavior. Due to its excellent performance, the algorithm has been widely used in engineering fields [17], e.g., a confined aquifer parameter inversion, Muskingum model parameter optimization [18], network flow prediction [19], and power system optimal power flow calculation [20]. Moreover, image segmentation is a key step in image analyzing and processing that transforms the original image into a more abstract and compact form, which makes it possible for higher-level image analysis [21].…”

Section: Introductionmentioning

confidence: 99%

A Scheme of Color Image Multithreshold Segmentation Based on Improved Moth-Flame Algorithm

et al. 2020

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A recently developed swarm intelligence algorithm by studying the natural moth's biological behavior is called Moth-Flame Optimization (MFO). The advantages of MFO conclude a simple structure and a robust selection capability. Still, it is easy to be trapped falling into optimal local, and slow search converges. This study suggests a new process improving MFO by hybridizing Lévy flight and logarithmic functions for its formula of flame updating to enhance the optimization performance of the algorithm. In the experimental section, a set of benchmark functions of CEC2013 and the multithreshold image segmentation are used to evaluate the proposed method performance. Compared results of the proposed methods with the different algorithms in the same condition scenarios show that the suggested approach provides better results than the various algorithms in the competitions.

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Multi-moth flame optimization for solving the link prediction problem in complex networks

Cited by 15 publications

References 39 publications

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Computational Modeling of Desiccation Properties (CW, LS, and VS) of Waste-Based Activated Ash-Treated Black Cotton Soil for Sustainable Subgrade Using Artificial Neural Network, Gray-Wolf, and Moth-Flame Optimization Techniques

A Scheme of Color Image Multithreshold Segmentation Based on Improved Moth-Flame Algorithm

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