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Network Scheduling Model of Cloud Computing based on particle swarm optimization algorithm

INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2015年第4期8卷 73-81页

作者： Lu, Ke Meng, Junxia Jiaozuo Univ Dept Int Educ Jiaozuo 454003 Peoples R China Jiaozuo Univ Coll Informat Engn Jiaozuo 454003 Peoples R China

The paper proposed a network scheduling in cloud computing based on intelligence particle swarm optimization algorithm aimed at the disadvantages of cloud computing network scheduling. Firstly, on the basis of cloud model, used intelligence particle swarm optimization algorithm with strong ability of global searching to find the better solution of cloud computing network scheduling then turned the better solution into the initial pheromone of improved particle swarm optimization algorithm, and found out the cloud computing network scheduling and the algorithm's global optimal solution through improved particle swarm optimization information communications and feedbacks. Finally, made comparison test of the three benchmark function on the basis of MATLAB, the results showed, compared with traditional intelligence particle swarm optimization algorithms, the improved algorithm can preferably allocate the resources in cloud computing model, the effect of prediction model time is more close to actual time, can efficiently limit the possibility of falling into local convergence, the optimal solution's time of objective function value is shorten which meet the user's needs more.

关键词： Network Computing Cloud Computing particle swarm optimization algorithm Network Scheduling

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Weighted particle swarm optimization algorithm for Randomized Unit Testing 1

Weighted Particle Swarm Optimization Algorithm for Randomize...

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IEEE International Conference on Electrical, Computer and Communication Technologies

作者： Dhivya, K. Devika Rani Meenakshi, V. S. Sri Krishna Arts & Sci Coll Dept CA & SS Coimbatore Tamil Nadu India Dr Mahalingam Coll Engn & Technol Dept Comp Applicat Pollachi India

ISBN: (纸本)9781479960859

Randomized testing is an effective method for testing software units. Thoroughness of randomized unit testing is according to the settings of optimal parameters. Randomized testing uses randomization for some aspects of test input data. Designing Genetic algorithm (GA) is somewhat of a black art. The feature subset selection (FSS) tool is used with GA to assess and to reduce the size and the content of the test case. FSS can be used to find and remove unnecessary parts of the search control automatically. The existing system does not cover all test data in test cases for the reason that it can quickly generate many test cases and does not consider the target method. Thus GA for Randomized unit testing has not achieves high coverage and does not produce better optimal test data. In the proposed method, particle swarm optimization (PSO) algorithm is used for randomized unit testing. PSO algorithm is used to evaluate the target method solutions for test coverage in test data. The main goal is to generate the optimal test parameter, to reduce the size of test case generation and to achieve high coverage of the units under test. PSO achieves high coverage and produce optimal value. PSO algorithm is enhanced weighted value. Weighted particle swarm optimization (WPSO) algorithm uses weight value in calculating the mean best position for each particle. It improves the efficiency of the system and achieves high coverage of the units under test within 5% of the time with better accuracy.

关键词： Randomized unit testing Genetic algorithm Feature Sub Set Selection particle swarm optimization algorithm Weighted particle swarm optimization algorithm

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State Variable Filter Design Using Improvised particle swarm optimization algorithm

State Variable Filter Design Using Improvised Particle Swarm...

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International Conference on Artificial Intelligence and Evolutionary algorithms in Engineering Systems (ICAEES)

作者： Indoria, Aakash Varrun, Varatharajan Akshay Reddy, Murali Krishna Sathyasai, Tejaswi Anand, Baskaran Devi, Nirmala M. Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amrita Sch Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9788132221357;9788132221340

State variable filter design using particle swarm optimization algorithm proves to be better when compared to the conventional design method. It gives several solutions to the component values which are useful in designing the state variable filter. The automatic termination technique gives the best possible solution in lesser time. This technique has several advantages in terms of a quicker convergence rate and efficient computation toward the suitable output, where an added advantage gives the user a control over the output's precision. The performance parameter here can be defined as the trade-off between the convergence time and accuracy of the resulting solution, which is determined by the precision value. The results also indicate that the solution with a predefined precision level can be obtained with the minimum number of iterations in minimum time.

关键词： particle swarm optimization algorithm Real-world optimization problems Complex optimization problems PSO algorithm

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P2P Traffic Identification Using Support Vector Machine and Cuckoo Search algorithm Combined with particle swarm optimization algorithm 1

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3rd CCF Internet Conference of China (CCF ICoC)

作者： Ye, Zhiwei Wang, Mingwei Wang, Chunzhi Xu, Hui Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (数字)9783662468265

ISBN: (纸本)9783662468265;9783662468258

As peer-to-peer (P2P) technology booms lots of problems arise such as rampant piracy, congestion, low quality etc. Thus, accurate identification of P2P traffic makes great sense for efficient network management. As one of the optimal classifiers, support vector machine (SVM) has been successfully used in P2P traffic identification. However, the performance of SVM is largely dependent on its parameters and the traditional tuning methods are inefficient. In the paper, a novel hybrid method to optimize parameters of SVM based on cuckoo search algorithm combined with particle swarm optimization algorithm is proposed. The first stage of the proposed approach is to tune the best parameters for SVM with training data. Subsequently, the SVM configured with the best parameters is employed to identify P2P traffic. In the end, we demonstrate the effectiveness of our approach on-campus traffic traces. Experimental results indicate that the proposed method outperforms SVM based on genetic algorithm, particle swarm optimization algorithm and cuckoo search algorithm.

关键词： P2P traffic identification Support vector machine Cuckoo search algorithm particle swarm optimization algorithm

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A Novel Quantum-behaved particle swarm optimization algorithm 14

A Novel Quantum-behaved Particle Swarm Optimization Algorith...

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International Symposium Distributed Computing and Applications Business Engineering and Science

作者： Zhao, Jing Liu, Hong Qilu Univ Technol Sch Informat Jinan Shandong Peoples R China Shandong Normal Univ Shandong Prov Key Lab Distributed Comp Software Jinan Peoples R China Shandong Normal Univ Shandong Prov Key Lab Distributed Comp Software Coll Informat Sci & Engn Jinan Peoples R China

ISBN: (纸本)9781467365932

A novel Quantum-behaved particle swarm optimization algorithm with probability (P-QPSO) is introduced to improve the global convergence property of QPSO. In the proposed algorithm, all the particles keep the original evolution with large probability, and do not update the position of particles with small probability, and re-initialize the position of particles with small probability. Seven benchmark functions are used to test the performance of P-QPSO. The results of experiment show that the proposed technique can increase diversity of population and converge more rapidly than other evolutionary computation methods.

关键词： particle swarm optimization algorithm quantum-behaved probability benchmark function

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An IoT-based English translation and teaching using particle swarm optimization and neural network algorithm

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SOFT COMPUTING 2023年第19期27卷 14431-14450页

作者： Zhang, Lili Anhui Univ Finance & Econ Sch Languages & Media Bengbu 233000 Anhui Peoples R China

English language translation teaching plays a crucial role in today's globalized world, particularly in countries like China, which has seen an increased emphasis on English language proficiency. However, effective teaching and assessment of students' translation abilities remain significant challenges for educators. To address the challenges of English language translation and teaching, this paper proposes a novel approach that integrates Internet of Things (IoT), particle swarm optimization (PSO), and Neural Network algorithms to assist teachers in evaluating and assessing students' English translation abilities by enabling them to better fulfill their teaching responsibilities. This paper first describes the computational mechanism of Neural Network algorithm using PSO, which includes the particle coding approach. An application model is designed to assess students' capacity for learning using English translational instruction. The PSO algorithm gathers information on their learning progress, and fully utilize the analyzed results to develop learning strategies and create teaching materials for various learning types, which is helpful for the quick advancement of English translation teaching. Secondly, this paper develops a translational application model for English language as a practical framework. The proposed method utilizes the upgraded PSO-enabled Neural Network to process English language translation and teaching data by achieving network training in a global optimal state of PSO by reducing training errors. Thirdly, this paper evaluates the effectiveness of the model by comparing the average errors of the training and test samples with different numbers of particles such as 5, 10, and 20. Finally, the results demonstrate high accuracy in measuring students' translation abilities. Furthermore, the study collects and examines three data sets, specifically English-Foreign Language Translation Corpus (EFLT), English Language Learners' Corpus (ELLC), and Engl

关键词： Internet of Things particle swarm optimization algorithm Neural network Network training English translation Smart classrooms Smart services

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Simulating particle swarm optimization algorithm to Estimate Likelihood Function of ARMA（1, 1） Model

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Journal of Mathematics and System Science 2015年第10期5卷 399-410页

作者： Basad Ali Hussain Al-sarray Laboratoire de Mathematiques de Besancon Universite de Franche-Comte Besancon cedex France.

This paper present a simulation study of an evolutionary algorithms, particle swarm optimization PSO algorithm to optimize likelihood function of ARMA（1, 1） model, where maximizing likelihood function is equivalent to maximizing its logarithm, so the objective function ＇***＇ is maximizing log-likelihood function. Monte Carlo method adapted for implementing and designing the experiments of this simulation. This study including a comparison among three versions of PSO algorithm “Constriction coefficient CCPSO, Inertia weight IWPSO, and Fully Informed FIPSO”, the experiments designed by setting different values of model parameters al, bs sample size n, moreover the parameters of PSO algorithms. MSE used as test statistic to measure the efficiency PSO to estimate model. The results show the ability of PSO to estimate ARMA＇ s parameters, and the minimum values of MSE getting for COPSO.

关键词： particle swarm optimization algorithm Likelihood function ARMA（1 1） Model

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A diversity enhanced hybrid particle swarm optimization and crow search algorithm for feature selection

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APPLIED INTELLIGENCE 2023年第17期53卷 20535-20560页

作者： Osei-kwakye, Jeremiah Han, Fei Amponsah, Alfred Adutwum Ling, Qing-Hua Abeo, Timothy Apasiba Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang Peoples R China Jiangsu Key Lab Secur Technol Ind Cyberspace Zhenjiang Peoples R China Univ Profess Studies Informat Technol Studies Dept Accra Ghana Univ Mines & Technol Comp Sci & Engn Dept Tarkwa Ghana Jiangsu Univ Sci & Technol Sch Comp Sci Zhenjiang Peoples R China Tamale Tech Univ Fac Appl Sci & Technol Dept Comp Sci Tamale Ghana

Feature Selection (FS) is choosing a subcategory of features purposed to construct a machine learning model. Among the copious existing FS algorithms, Binary particle swarm optimization algorithm (BPSO) is prevalent with applications in several domains. However, BPSO suffers from premature convergence that affects exploration, resulting in dilapidation. In this current work, we boost the exploration of BPSO, incorporating the intelligence of crows to hide their food sources from other crows and predators and maintain diversity by implementing a clustering strategy. The clustering technique guarantees that the starting population is evenly distributed over the feature space while including more promising features. Additionally, suppose a crow realizes another crow or a predator is tracking it. In that case, the crow moves randomly to evict the stalker, leading to a better exploration of unexplored regions within the search space. We named the proposed method Hybrid particle swarm optimization and Crow Search algorithm with clustering initialization strategy (HPSOCSA-CIS). To evaluate the performance of HPSOCSA-CIS, 15 standard UCI datasets are utilized, and the outcomes are compared with recently proposed hybrid and standard optimization algorithms. From observation, HPSOCSA-CIS outperforms the comparing approaches for feature selection challenges on representative datasets that fall in the three-category based on dimensions. The HPSOCSA-CIS improves performance in terms of mean classification accuracy by 8.87%, 17.5%, and 21.90% on Low, medium, and high dimensional datasets, respectively.

关键词： particle swarm optimization algorithm Crow search algorithm Clustering Feature selection

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An artificial-intelligence-driven product design framework with a synergistic combination of Genetic algorithm and particle swarm optimization

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SOFT COMPUTING 2023年第23期27卷 17621-17638页

作者： Liu, Yuge Kim, Kiesu Silla Univ Sch Design Busan 46958 South Korea

In recent years, China's rapid advancements in artificial intelligence (AI) technology have positioned it as a global leader in information processing, intelligent chip development, and deep learning technologies. The pervasive impact of AI has led to transformative shifts in various domains of life, with its integration across industries substantially enhancing overall production efficiency. Particularly evident in product design, the infusion of AI technology has catalyzed significant productivity improvements. This research investigates the amalgamation of Genetic algorithms (GA) and particle swarm optimization (PSO) within the framework of AI technology to establish a robust product design system. The system aims to enhance the efficiency and efficacy of design processes by leveraging the complementary strengths of GA and PSO. This integration has propelled product design into accelerated evolution and modernization. The study delves into AI's application of deep neural networks in product design within the context of the digital age. Artificial intelligence technology is used in product design in the context of the Internet. First, the concept of AI technology is briefly introduced, and DNN algorithms are described in detail, including the PSO and Genetic algorithms. Second, the product design and development process based on artificial intelligence technology, design guidelines, and technical features are mainly described. Finally, using the seat as the research object, the application effect of artificial intelligence technology in product design is analyzed, and 10 subjective evaluation questions are set to experiment. Regarding material texture, the highest score for the B3 Barcelona chair is 4.3. Users prefer leather seats with diverse materials, and the score for painted wood materials is lower. From the product structure and shape analysis, the highest score is the B3 Barcelona chair, which has a value of 4.1, and the lowest score is the B1 red and blue

关键词： Artificial intelligence Internet particle swarm optimization algorithm Genetic algorithm Product design

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Calculation and optimization of Electric Helicopter Flight Performance Based on particle swarm algorithm 36

Calculation and Optimization of Electric Helicopter Flight P...

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36th Chinese Control and Decision Conference (CCDC)

作者： Ji, Ming Qi, Yiwen Zhang, Qingxin Wang, Feng Zhou, Guoqing Lv, Yanbo Shenyang Aerosp Univ Sch Automat Shenyang 110136 Peoples R China Liaoning Gen Aviat Acad Shenyang 110136 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

As energy issues become increasingly prominent, the electrification of aviation aircraft has gradually become a research hotspot, and electric helicopters are also in a stage of rapid development. Based on the flight performance analysis method of fuel-powered helicopters and combined with the characteristics of electric power system, this paper proposes a calculation method for the flight performance of electric helicopters and establishes an analysis model. This model can demonstrate the performance of electric helicopters in three aspects: vertical flight, climbing flight and horizontal flight. Then, the particle swarm optimization (PSO) algorithm is used to optimize the overall flight performance of the helicopter. It can allocate weights reasonably according to design requirements and selectively optimize key performance indicators. The results show that the adopted optimization algorithm has good effect and applicability to the overall parameters optimization problem of electric helicopters.

关键词： Electric helicopters Flight performance analysis particle swarm optimization algorithm Overall parameters optimization

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