Cloud computing is an emerging technology in distributed computing, which facilitates pay per model as per user demand and requirement. Cloud consists of a collection of virtual machines (VMs), which includes both com...
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Cloud computing is an emerging technology in distributed computing, which facilitates pay per model as per user demand and requirement. Cloud consists of a collection of virtual machines (VMs), which includes both computational and storage facility. In this paper, a task scheduling scheme on diverse computing systems using a hybridization of genetic and groupsearchoptimization (GGSO) algorithm is proposed. The basic idea of our approach is to exploit the advantages of both genetic algorithm (GA) and group search optimization algorithms (GSO) while avoiding their drawbacks. In GGSO, each dimension of a solution symbolizes a task, and a solution, as a whole, signifies all task priorities. The important issue is how to assign user tasks to maximize the income of infrastructure as a service (Iaas) provider while promising quality of service (QoS). The generated solution is competent to assure user-level (QoS) and improve Iaas providers' credibility and economic benefit. The GGSO method also designs the producer, scrounger ranger, crossover operator, and suitable fitness function of the corresponding task. According to the evolved results, it has been found that our algorithm always outperforms the traditional algorithms.
With the development of science and technology, battery energy plays an increasingly important role in modern society. The role of battery energy includes but is not limited to providing power for mobile devices and p...
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ISBN:
(纸本)9798350317022
With the development of science and technology, battery energy plays an increasingly important role in modern society. The role of battery energy includes but is not limited to providing power for mobile devices and powering cars. It is also the main energy source for new energy vehicles. Energy storage systems provide energy storage. It provides power for aerospace and can also be used as a backup power supply for households and industries to deal with sudden power outages and other situations. Neural networks are also widely used in battery energy. For example, neural networks can be used to predict the remaining service life of lithium-ion batteries. This paper takes the commonly used 18650 power lithium battery as the main research object, and proposes a neural network battery state of charge (SOC) estimation method based on the LEVY flight step-based group search optimization algorithm (LEVY-GSO-BP). The group search optimization algorithm after Levy flight step optimization is used to optimize the design of the established SOC neural network model to improve the accuracy of the neural network's estimation of SOC related parameters. BP neural network is a powerful optimizationalgorithm that can solve unimodal function and multimodal function optimization problems of high-dimensional data. It has the advantages of simplicity, efficiency and accuracy, so it is widely used in various problems. However, the calculation of BP neural network is more difficult, takes a long time, and the convergence speed drops significantly at the end of the calculation, and may even stagnate. In order to solve these problems, we propose an algorithm improvement method based on Levy flight step size. Levy flight is an optimizationalgorithm with excellent global search capabilities that can find the optimal solution in a short time. It uses a combination of short-distance frequent search and occasional jumping long-distance search to improve local search capabilities and global sear
In terms of training students for work in diverse firms, traditional and out-of-date teaching techniques cannot compete with digital teaching methods. To overcome this problem, the teaching approach and content must b...
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In terms of training students for work in diverse firms, traditional and out-of-date teaching techniques cannot compete with digital teaching methods. To overcome this problem, the teaching approach and content must be changed. An Educational Assistant for Software Testing (EAST) framework is developed in this work to train students to improve their skills in software testing via Computer Assisted Instruction (CAI) built using Natural Language Processing (NLP), Machine learning, and information retrieval techniques. In this paper, a groupsearch Optimized two-stage hybrid Support Vector Machine-K-Nearest Neighbor (SVM-KNN) classifier is used to develop a novel approach for analyzing the parameters that introduce bugs in bug reports. To decrease the data sparsity problem, the groupsearchoptimization (GSO) algorithm is used to improve the parameter selection process of the two-stage hybrid classifier by generating optimal values for parameters such as k, c, and gamma. Two bug report datasets were used to test the model. The database for our application is built by collecting bug reports from a wide open-source community as well as several mobile application development companies. Based on the extensive experiments conducted via different performance metrics, we can conclude that the EAST framework can improve outdated teaching methodologies.
The power-voltage (P-U) curve of PV array shows multiple power points, which brings challenges to fast and accurately tracking of the global maximum power point. Considering the nonlinearity and the multi-peak charact...
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The power-voltage (P-U) curve of PV array shows multiple power points, which brings challenges to fast and accurately tracking of the global maximum power point. Considering the nonlinearity and the multi-peak characteristics of PV array output curve under the condition of partial shadow, a multi-producer groupsearchoptimization (MGSO) method for maximum power point tracking (MPPT) is proposed in this paper. In the MGSO, the characteristics and operations of three categories of members, including producers, scroungers, and rangers, are set according to the P-U characteristics. The number of producers is determined by the number of peaks. The initial position of each producer locates dispersedly to each peak region which makes the producers not fall into local optimum. The search strategy is simplified by the proposed angle transformation function of scroungers and omitting the ranger. The results of simulation and comparison demonstrate that the proposed MGSO method can effectively track the maximum power point under the uniform irradiance or partially shaded conditions, and also increase the utilization of solar energy.
This paper introduces a novel optimizationalgorithm, groupsearch optimizer (GSO) algorithm. The implementation method of this algorithm is presented in detail. The GSO is used to investigate the truss structures wit...
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This paper introduces a novel optimizationalgorithm, groupsearch optimizer (GSO) algorithm. The implementation method of this algorithm is presented in detail. The GSO is used to investigate the truss structures with continuous variables and was tested by three truss structure optimization problems. The optimization results were compared with those of the particle swarm optimizer (PSO), the particle swarm optimizer with passive congregation (PSOPC) and the heuristic particle swarm optimizer (HPSO). The calculation results show that the GSO has preferable convergence rate and accuracy. Results from the three tested cases illustrate the competitive ability of the GSO to find the optimal results. It is desired for GSO to be used for structural optimal design problems.
This paper introduces a novel optimizationalgorithm, groupsearchoptimization (GSO) algorithm. The implementation method of this algorithm is presented in detail. The GSO is used to investigate truss structures with...
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ISBN:
(纸本)9787030227058
This paper introduces a novel optimizationalgorithm, groupsearchoptimization (GSO) algorithm. The implementation method of this algorithm is presented in detail. The GSO is used to investigate truss structures with continuous variables and was tested by a planar truss optimization problem. The optimization results were compared with that of the particle swarm optimizer (***), the particle swarm optimizer with passive congregation (PSOPC) and the heuristic particle swarm optimizer (HPSO). Results from the tested case illustrate the ability of the GSO to find the optimal results. Test results show GSO has better convergence rate than that of the PSO and PSOPC, and has the same convergence level as that of HPSO.
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