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Priority-based task scheduling method over cloudlet using a swarm intelligence algorithm

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2020年第2期23卷 663-671页

作者： Milan, Sara Tabagchi Rajabion, Lila Darwesh, Aso Hosseinzadeh, Mehdi Navimipour, Nima Jafari Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran Univ South Florida Sarasota Manatee Coll Business Sarasota FL USA Univ Human Dev Informat Technol Dept Sulaymaniyah Iraq Iran Univ Med Sci Hlth Management & Econ Res Ctr Tehran Iran Univ Human Dev Comp Sci Sulaymaniyah Iraq Islamic Azad Univ Islamshahr Branch Young Researchers & Elite Club Islamshahr Iran

In a cloud environment, scheduling problem as an NP-complete problem can be solved using various metaheuristic algorithms. The metaheuristic algorithms are very popular for scheduling tasks because of their effectiveness. A bacterial foraging is a swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. This paper proposes a task scheduling algorithm based on bacterial foraging optimization to reduce the idle time of virtual machines whereas the load balancing and reducing of runtime have occurred. The Cloudsim toolkit has assessed the performance of the proposed method in comparison with some scheduling algorithms. According to the obtained results, the makespan and energy consumption were reduced by using the proposed algorithm.

关键词： Cloud computing Scheduling algorithms Energy swarm intelligence algorithm

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Using swarm intelligence algorithm to solve a part clustering model based on weighted directed graph

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2008年第7期46卷 1911-1932页

作者： Zhao, G. Jiang, P. Zheng, M. Zhou, G. Chen, B. Xian Jiaotong Univ Sch Mech Engn State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

Mass customisation is an effective mechanism to satisfy the new requirements of changing markets. E-manufacturing can be seen as a good choice to implement mass customisation. As a support technology for mass customisation, part clustering of the product family is very important in the optimal formation of manufacturing cells or systems and it could be more efficient in e-manufacturing mode than in traditional manufacturing mode. This paper is mainly concerned with the part clustering problems for the manufacturing of mass customisation. A mathematical model of part clustering of a product family based on weighted directed graph is put forward. According to the criterion of minimising the manufacturing cost, two hierarchic sub-models and a relevant methodology are presented. As the bottleneck problem of the methodology, process combination is realised by combining the incidence matrixes of the abovementioned weighted directed graph model. Subsequently a swarm intelligence algorithmic model is designed and a software prototype system is developed with java technology. Finally,a clustering case is put forward and studied, which illustrates that the models described in this paper can be used to solve the parts clustering problem in e-manufacturing system for mass customisation effectively.

关键词： e-manufacturing mass customisation clustering analyses weighted directed graph swarm intelligence algorithm

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Adaptive online data-driven closed-loop parameter control strategy for swarm intelligence algorithm

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INFORMATION SCIENCES 2020年 536卷 25-52页

作者： Lu, Hui Liu, Yaxian Cheng, Shi Shi, Yuhui Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710062 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

Parameter control is critical for the performance of any swarm intelligence algorithm. In this study, we propose an adaptive online data-driven closed-loop parameter control (CLPC) strategy for a swarm intelligence algorithm to solve both single-objective and multi-objective optimization problems with better performance. The proposed CLPC strategy involves three key parts: controller design, feedback selection, and reference determination. First, based on the control theory, we adopt a proportional integral derivative (PID) controller in the CLPC strategy, which can adaptively adjust the value of parameter according to the difference between reference and feedback. Second, to reflect and monitor the evolution state in real time, we use the mean shift clustering method and define the convergence entropy and the extension entropy to generate feedback. Finally, the reference should provide useful guidance for parameter control according to the features of optimization problems. Thus, in single-objective optimization, we propose a new lossless fitness landscape analysis method and design a decision tree to determine the reference;in multi-objective optimization, the range of the convergence entropy and the extension entropy are regarded as the reference. In addition, to illustrate the effectiveness of the CLPC strategy, two groups of experiments are performed based on the particle swarm optimization (PSO) algorithm in single-objective and multi-objective optimization. At the optimization algorithm level, we compare our proposed CLPC-PSO algorithm with three standard PSO algorithms, three decreasing inertia weight PSO algorithms, and two adaptive PSO algorithms. At the optimization problem level, we perform abundant experiments based on five single-objective benchmark functions, five multi-objective benchmark functions, and twelve scheduling instances. The statistical results show that the performance of the proposed CLPC-PSO algorithm is considerably better and more sta

关键词： swarm intelligence algorithm Closed-loop parameter control strategy PID control system Population clustering Convergence and extension entropy

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A swarm intelligence algorithm based game theory

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2013年第3期4卷 287-297页

作者： Bai, Yan-ping Zhang, Yu Capital Normal Univ Sch Management Beijing 100048 Peoples R China China Univ Min & Technol State Key Lab GeoMechan & Deep Underground Engn Beijing 100083 Peoples R China Beijing Univ Civil Engn & Architecture Dept Comp Teaching & Network Informat Beijing 100044 Peoples R China

In this paper, we established a relationship between particle swarm optimisation algorithms and game theory. On that basis, a swarm intelligence-based search mechanism is proposed and applied to solving the attribute reduction problem in the context of rough sets. The proposed attribute reduction algorithm can set up different participatory groups and game strategies, construct corresponding pay utility matrix, and produce optimal combinations through gaming procedure. Numerical experiments on a number of UCI datasets show the proposed game strategies-based reduction algorithm is superior to particle swarm optimisation, tabu search, gene algorithm and PSO with mutation operator in terms of solution quality, and has lower computational cost.

关键词： swarm intelligence algorithm rough set pay utility matrix game computing science

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Research on the optimum synchronous network search data extraction based on swarm intelligence algorithm

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 125卷 151-155页

作者： Hu, Su Yin, Hua Guangdong Univ Finance & Econ Sch Informat Sci Guangzhou 510320 Guangdong Peoples R China

Existing synchronous network search data extraction techniques suffer from load unbalance in the face of increasing number of concurrent users. For this reason, this paper presents the research on the optimum synchronous network search data extraction based on swarm intelligence algorithm. A traffic balancing model is constructed to determine the node balance state according to the traffic variation and influence factors of network nodes. Under the condition that the node state is known, the swarm intelligence algorithm is used to cluster the data to be synchronized and adjust the node state so that it is kept stable throughout the synchronization process. The clustered data act as the target to connect the user with the server side to achieve the optimum network search data extraction and synchronization. The experimental results show that when the number of concurrent network users is increasing, the designed technique features stable load balancing, and achieves optimum data extraction performance and low execution cost when the task completion time is less than 0.5 s. (C) 2021 Elsevier B.V. All rights reserved.

关键词： swarm intelligence algorithm Network search Data extraction Synchronous update

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An improved swarm intelligence algorithm for solving TSP problem

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3rd International Conference on Intelligent Computing

作者： Tao, Yong-Qin Cui, Du-Wu Miao, Xiang-Lin Chen, Hao Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

ISBN: (纸本)9783540742012

Traveling Salesman Problem (TSP) is a typical NP-Complete problem. This paper, through finding the solution of TSP, combining the use of high-efficiency gene regulatory algorithm, particle swarm optimization and ant colony optimization, proposes a kind of improved swarm intelligence algorithm GRPSAC. The GRPSAC overcomes the disadvantages of several algorithms through the use of the crossover, the mutation and the gene regulation. The experimental results indicate that GRPSAC not only has a high-efficiency, but also induces better optimal results.

关键词： swarm intelligence algorithm gene regulation tsp

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A New Discrete Eel swarm intelligence algorithm

A New Discrete Eel Swarm Intelligence Algorithm

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4th IEEE International Conference on Information Science and Technology (ICIST)

作者： Sun Yao-sheng Huang Zhang-can Chen Yu Yuan Chao Wuhan Univ Technol Sch Sci Wuhan 430070 Hubei Province Peoples R China

ISBN: (纸本)9781479948086

Inspired by the migration behaviours of eels, we propose a new discrete eel swarm intelligence algorithm in this paper. First, this paper analyzes the behaviours of eels, then establishes a mathematical model of the important behaviour being extracted. Based on rational organization of three important behaviours of eel, namely concentration adaptation, neighbourhood learning and gender mutation, the discrete eel swarm intelligence algorithm is proposed for combinatorial optimization problems. Finally, numerical experiments on the TSP problem and the permutation flow-shop scheduling problem show that the algorithm has strong optimization capability and accuracy.

关键词： swarm intelligence algorithm optimization problem concentration adaptation neighborhood learning gender mutation

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Intelligent Scheduling Technology of swarm intelligence algorithm for Drone Path Planning

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DRONES 2024年第4期8卷 120-120页

作者： Meng, Zhipeng Li, Dongze Zhang, Yong Yan, Haoquan Acad Mil Sci Natl Innovat Inst Def Technol Beijing 100071 Peoples R China Nanjing Univ Aeronaut & Astronaut Res Inst Pilotless Aircraft Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Nanjing 210016 Peoples R China

Different kinds of swarm intelligence algorithm obtain superior performances in solving complex optimization problems and have been widely used in path planning of drones. Due to their own characteristics, the optimization results may vary greatly in different dynamic environments. In this paper, a scheduling technology for swarm intelligence algorithms based on deep Q-learning is proposed to intelligently select algorithms to realize 3D path planning. It builds a unique path point database and two basic principles are proposed to guide model training. Path planning and network learning are separated by the proposed separation principle and the optimal selection principle ensures convergence of the model. Aiming at the problem of reward sparsity, the comprehensive cost of each path point in the whole track sequence is regarded as a dynamic reward. Through the investigation of dynamic environment conditions such as different distances and threats, the effectiveness of the proposed method is validated.

关键词： algorithm scheduling deep reinforcement learning path planning swarm intelligence algorithm

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Welding sequence optimization to reduce welding distortion based on coupled artificial neural network and swarm intelligence algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL intelligence 2022年第0期114卷

作者： Wu, Chunbiao Wang, Chao Kim, Jae-Woong Changshu Inst Technol Sch Mech Engn Changshu 215500 Jiangsu Peoples R China Yeungnam Univ Sch Mech Engn Gyongsan 38541 Gyeongbuk South Korea

This study aims to develop a welding sequence optimization (WSO) framework based on coupled artificial neural network (ANN) and swarm intelligence algorithm for minimizing welding distortion of thin-walled squared Al-Mg-Si alloy tube components. This framework is mainly composed of two critical computer programs. Firstly, a multilayer feedforward backpropagation neural network (BPNN) system was established to rapidly estimate residual distortion for an arbitrary welding sequence so that welding sequence can be optimized for achieving desired welding quality. For this purpose, a series of nonlinear thermo-elastic-plastic finite element (FE) simulations were conducted and verified with experiments to generate the input database of the neural network. Subsequently, a reliable BPNN model was successfully created and trained within an acceptable error. Secondly, a novel swarm intelligence algorithm, namely, bees algorithm (BA) was proposed to solve the complicated WSO problems. In this optimization process, the trained BPNN model was implanted into this proposed BA for computing the fitness value of arbitrary welding sequences. Moreover, welding experiments were also performed to confirm the performance of the proposed optimization method. Comparing the results from experimental measurements, FE simulations, and proposed WSO framework, it is demonstrated that this proposed BPNN-and-BA-based WSO framework can be successfully applied in practical engineering to obtain an optimal welding sequence for minimizing final welding distortion.

关键词： Welding sequence optimization (WSO) Artificial neural network (ANN) Backpropagation neural network (BPNN) swarm intelligence algorithm Bees algorithm (BA)

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Prediction of mechanical properties of manufactured sand polymer-modified mortar based on swarm intelligence algorithm

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JOURNAL OF BUILDING ENGINEERING 2024年 98卷

作者： Tian, Haozheng Zhang, Yunsheng Qiao, Hongxia Feng, Qiong Wang, Penghui Lanzhou Univ Technol Sch Civil Engn Lanzhou 730050 Peoples R China Lanzhou Univ Technol Western Minist Civil Engn Disaster Prevent & Mitig Lanzhou 730050 Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Guangdong Prov Key Lab Durabil Marine Civil Engn Shenzhen 518060 Peoples R China

Because polymer-modified mortar (PMM) exhibits a complex and diverse composition, there is a complex nonlinear relationship between its mechanical properties and mix proportions that is challenging for conventional mechanical performance prediction methods to accurately predict. Consequently, in this study, a predictive model utilizing a backpropagation neural network (BPNN) was formulated, featuring a structure of 6-14-2. Simultaneously, three swarm intelligence algorithms were integrated-the ant colony optimization algorithm, grey wolf optimization algorithm, and bat optimization algorithm (BAT)-to collectively refine the optimization process of the BPNN prediction model. The model's input layer included cement (OPC), cellulose ether (CE), dispersible polymer powder (DPP), antifoam (AF), fly ash (FA), and tuff stone powder (SP), and the output layer consisted of the compressive and bond strengths. The model dataset comprised 520 samples (260 x 2), with 60 % (312) used for model establishment and 40 % (208) used for validation. Correlation matrix and principal component analyses were conducted on the dataset, along with a comparative analysis of the factors influencing the mechanical performance evaluation indicators. The results indicate that at 7 and 28 d, there was a positive correlation between the DPP and AF with the development of PMM mechanical properties. At 7 d, the SP and FA were negatively correlated with the compressive and flexural strength, whereas the CE was positively correlated with the bond strength. At 28 d, the OPC was negatively correlated with the compressive, bond, and flexural strengths, and positively correlated with the SP and FA. C3 represents the optimal mix proportion for the PMM, and considering the influence of all raw materials, F3 was identified as the comprehensive optimal mix proportion. The predictive performance evaluation indicators of the BAT-BPNN for the compressive and bond strengths were R2 = 0.980 and 0.942, MAE = 5.967 an

关键词： Polymer-modified mortar swarm intelligence algorithm Mechanical properties BAT-BPNN Manufactured sand

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