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A new approach for service migration in cloud-based e-commerce using an optimization algorithm

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2020年第14期33卷 e4457-e4457页

作者： Huang, Lixia Abnoosian, Karlo Hunan Appl Technol Univ Coll Informat Engn Changde Hunan Peoples R China Islamic Azad Univ Dept Stat Sci & Res Branch Tehran Iran

Service-oriented architecture (SOA) has a crucial role in backing productive cloud services. Also, the vast spread of the theoretical notion of diverse businesses (like e-commerce) into the actual use has been recently applied by cloud computing. The service functionality could be affected by overfilling of the network traffic because of the broadly dispersed nature of e-commerce in clouds-a key challenge for immediate jobs. Throughout the last decade, a vast range of applications or large-scale operators has increasingly attracted to migrate the services in clouds. An effective method for accessing the applications throughout standard business hours is continually moving virtual machine containers from one data center to another. Now, with the commonness of cloud computing, many applications have been moved to the cloud fully/partly. It can be handled through the migration of cloud services to diverse platforms in a way that minimizes the communication cost of e-commerce. As this issue has an NP-hard nature, in the present article, we present an automatic smart service migration outline through the ant colony optimization (ACO) algorithm on cloud-oriented e-commerce. In the presented model, we use the ACO algorithm to take the finest (near-optimal) service migration decisions. Based on the obtained results, the proposed technique has the optimal number of migrations compared to the existing models.

关键词： ant colony optimization algorithm cloud computing e-commerce service migration

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Definition of a New Metric With Mutual Exclusivity and Coverage for Identifying Cancer Driver Modules

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IEEE ACCESS 2020年 8卷 133767-133776页

作者： Zhang, Wei Liu, Yue Lei, Wang Lei Wang Changsha Univ Hunan Prov Key Lab Ind Internet Technol & Secur Changsha 410022 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

Identification of cancer driver modules or pathways is a key step in understanding cancer pathogenesis and exploring patient-specific treatments. Numerous studies have shown that some genes with low mutation frequency are also important for the cancer progression, while previous research have focused on identifying high-frequency mutation genes. In this study, we propose a new framework with a new metric to identify driver modules with low-frequency mutation genes, called iCDModule. Inspired by the gravity model, we integrate the coverage and mutual exclusivity in mutation information, define a new metric between gene pairs, called mutation impact distance, to help identifying potential driver genes sets, including those have extremely low mutation rates but play an important role in functional networks. A genetic network is constructed by combining the defined mutation impact distance and then the driver module identification problem is formalized as the maximum clique solution problem, and an improved ant colony optimization algorithm is used to solve it. iCDModule is applied to TCGA breast cancer, glioblastoma, ovarian cancer to test performance. Experiments show that it can accurately identify known cancer driver modules and pathways, and also detect driver modules containing low-frequency mutation genes. iCDModule is significantly better than other existing methods in identifying driver modules.

关键词： Cancer Mathematical model Measurement Gravity ant colony optimization Web servers Genetics Driver modules mutual exclusivity mutation impact distance low-frequency mutation ant colony optimization algorithm

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Cluster-based data transmission scheme in wireless sensor networks using black hole and ant colony algorithms

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第9期34卷 e4768-e4768页

作者： Sefati, Seyedsalar Abdi, Mehrdad Ghaffari, Ali Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran

Since sensor nodes use limited battery power, sensor nodes power is considered to be a challenge and fundamental issue in wireless networks. Wireless sensor networks (WSNs) have recently used clustering-based methods and different routing protocols in clusters which are full of sensor nodes. In this way, cluster heads (CHs), which are regarded as the selected nodes among other nodes within a given cluster, accumulate the information transmitted from their own cluster and send it to the sink. In this method, they try to control power consumption in a balanced way. In clustering method and routing among network nodes, network lifetime is enhanced which leads to achieving the best efficiency and productivity. In this paper, the optimized black hole algorithm is employed to select an optimal CH from sensor nodes. The CH selection is optimized by the free buffer of nodes, residual energy, and distance. The path between source CH and sink is identified by using ant colony optimization (ACO) algorithm. The combination of black hole algorithm and ant colony algorithm, with respect to clustering and routing, leads to the optimization of the proposed method in terms of operational criteria such as power consumption and eventually lifetime enhancement. The results obtained from simulating the proposed method in Matlab environment indicated that it has better results in comparison with other methods. The outputs of the obtained results from the proposed method indicated that it outperformed the other four comparative methods in terms of packet delivery rate and the number of transmitted packets to the CH and to the sink.

关键词： ant colony optimization algorithm black hole algorithm clustering routing wireless sensor network (WSNs)

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HYBRID optimization OF OBJECT-BASED CLASSIFICATION IN HIGH-RESOLUTION IMAGES USING CONTINOUS ant colony algorithm WITH EMPHASIS ON BUILDING DETECTION 2

HYBRID OPTIMIZATION OF OBJECT-BASED CLASSIFICATION IN HIGH-R...

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ISPRS Int Joint Conf of 2nd Conf on Geospatial Information Research GI Research, 4th Conf on Sensors and Models in Photogrammetry and Remote Sensing (SMPR), 6th Conf on Earth Observation of Environmental Changes (EOEC)

作者： Tamimi, E. Ebadi, H. Kiani, A. KN Toosi Univ Technol Fac Geodesy & Geomat Engn Tehran Iran

Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous ant colony optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

关键词： Hybrid optimization Object-based Classification ant colony optimization algorithm High Spatial Resolution Image

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Blockchain for consortium: A practical paradigm in agricultural supply chain system

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 184卷 115425-115425页

作者： Kyzy, Indra Eluubek Song, Huaming Vajdi, Ahmadreza Wang, Yongli Zhou, Junlong Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

Trading aspect of agricultural supply chain system is sophisticated since it consists of many stages and involves various entities/agencies. Recently, blockchain technology could prove its effectiveness to solve some of the concerns in agricultural supply chain systems. Nevertheless, maximizing profit for producers (in our study farmers) is another important concern that can be addressed by consortium establishment which blockchain technology is the best solution for this purpose due to the following reasons. First, since all the nodes in the blockchain keep the verified and synchronized version of the chain, each node can verify the transactions' transparency. Second, blockchain technology is temper-proof that means no one can change the history of the transactions. These two main features of blockchain technology can provide a suitable ground to construct a consortium among the producers. However, there are other specific requirements that a successful consortium in agricultural supply chain system should address them that motivate us to a new design of blockchain technology. More precisely, in our design we consider the problems of trustability, scalability, and share amount assignment. For trustability, we utilize cyber physical system to ensure the quantity and quality of the products. Scalability is being addressed by adopting the concept of public service platform and proposing a new consensus algorithm. And finally share amount assignment is being solved by our improved version of ant colony optimization algorithm. Experimental results and analysis prove the effectiveness and accuracy of our proposed design for blockchain technology.

关键词： Blockchain ant colony optimization algorithm Agricultural supply chain system Cyber physical system

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Route Planning of Helicopters Spraying Operations in Multiple Forest Areas

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FORESTS 2021年第12期12卷 1658页

作者： Fang, Shuping Ru, Yu Liu, Yangyang Hu, Chenming Chen, Xuyang Liu, Bin Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing 210037 Jiangsu Peoples R China Anhui Sci & Technol Univ Coll Mech Engn Chuzhou 233100 Peoples R China Anhui Agr Univ Sch Engn Hefei 230036 Anhui Peoples R China

It is of great value to research the problem of forest pest and disease control. Currently, helicopters play an important role in dealing with this problem. However, the spraying route planning still depends on the pilot's driving experience, which leads to low efficiency and less accurate coverage. For this reason, this paper attempts to use intelligent algorithms to plan the pesticide spraying route for helicopters. When the helicopter is conducting spraying operations in multiple forest areas, the routes are divided into two parts: pesticide spraying routes for individual forest areas and dispatch routes between multiple forest areas. First, the shorter spraying route with fewer turnarounds for individual forest areas was determined. Then a two-layer intelligent algorithm, a combination of a genetic algorithm (GA) and ant colony optimization algorithm (ACO), was designed to determine the dispatch route between multiple forest areas, which is referred to as GAACO-GA. The performance was evaluated in self-created multiple forest areas and compared with other two-layer intelligent algorithms. The results show that the GAACO-GA algorithm found the shortest dispatch route (5032.75 m), which was 5.60%, 5.45%, 6.54%, and 4.07% shorter than that of GA-GA algorithm, simulated annealing-GA (SA-GA) algorithm, ACO-GA algorithm, and particle swarm optimization-GA (PSO-GA) algorithm, respectively. A spraying experiment with a helicopter was conducted near Pigzui Mountain, Huai'an City Jiangsu Province, China. It was found that the flight path obtained from the proposed algorithm was 5.43% shorter than that derived from a manual planning method. The dispatch route length was reduced by 16.93%, the number of turnarounds was reduced by 11 times, and the redundant coverage was reduced by 17.87%. Moreover, helicopter fuel consumption and pesticide consumption decreased by 10.56% and 5.43%, respectively. The proposed algorithm can shorten the application route, reduce the number of

关键词： route planning helicopter two-layer intelligent algorithm genetic algorithm ant colony optimization algorithm multiple forest areas spraying operations

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EFFECTIVE CLUSTER-FIRST ROUTE-SECOND APPROACHES USING METAHEURISTIC algorithmS FOR THE CAPACITATED VEHICLE ROUTING PROBLEM

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2021年第1期28卷 14-38页

作者： Comert, Serap Ercan Yazgan, Harun Resit Sakarya Univ Dept Ind Engn Sakarya Turkey

In this paper, three cluster-first route-second approaches are proposed to solve the capacitated vehicle routing problem (CVRP) that extends a traveling salesman problem (TSP). In the first phase, a giant tour covering all customers is built using three different metaheuristic algorithms as an ACO, a GA, and an ABCA. Then, the giant tour is split with respecting the vehicle capacity, and vehicles are loaded. In the second phase, we transform our problem into a small TSP after completing the clustering process, and a routing problem is solved based on a Branch-and-Bound algorithm. We evaluate the performance of these approaches on the benchmark problems. The computational results show that these approaches achieve high-quality results and gain an advantage in terms of CPU time. Besides, these approaches are also applied to a real-life case study related to a distribution CVRP meeting the weekly demands of a supermarket chain and provide a better routing solution.

关键词： cluster-first route-second approach capacitated vehicle routing problem ant colony optimization algorithm genetic algorithm artificial bee colony algorithm

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Towards Efficient Milling of Multi-Cavity Aeronautical Structural Parts Considering ACO-Based Optimal Tool Feed Position and Path

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MICROMACHINES 2021年第1期12卷 88-88页

作者： Xin, Yupeng Li, Yuanheng Li, Wenhui Wang, Gangfeng Taiyuan Univ Technol Coll Mech & Vehicle Engn Taiyuan 030024 Peoples R China Taiyuan Univ Technol Coll Aeronaut & Astronaut Taiyuan 030024 Peoples R China Changan Univ Sch Construct Machinery Key Lab Expressway Construct Machinery Shaanxi Pr Xian 710064 Peoples R China

Cavities are typical features in aeronautical structural parts and molds. For high-speed milling of multi-cavity parts, a reasonable processing sequence planning can significantly affect the machining accuracy and efficiency. This paper proposes an improved continuous peripheral milling method for multi-cavity based on ant colony optimization algorithm (ACO). Firstly, by analyzing the mathematical model of cavity corner milling process, the geometric center of the corner is selected as the initial tool feed position. Subsequently, the tool path is globally optimized through ant colony dissemination and pheromone perception for path solution of multi-cavity milling. With the advantages of ant colony parallel search and pheromone positive feedback, the searching efficiency of the global shortest processing path is effectively improved. Finally, the milling programming of an aeronautical structural part is taken as a sample to verify the effectiveness of the proposed methodology. Compared with zigzag milling and genetic algorithm (GA)-based peripheral milling modes in the computer aided manufacturing (CAM) software, the results show that the ACO-based methodology can shorten the milling time of a sample part by more than 13%.

关键词： smart manufacturing complex structural parts processing sequence planning corner milling ant colony optimization algorithm

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Task Offloading Scheduling in Mobile Edge Computing Networks

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Procedia Computer Science 2021年 184卷 322-329页

作者： Zhonglun Wang Peifeng Li Shuai Shen Kun Yang Quzhou Research Institute of University of Electronic Science and Technology of China Quzhou Zhejiang China University of Electronic Science and Technology of China Chengdu 611731 China School of Computer Science and Electronic Engineering University of Essex Colchester CO4 3SQ U.K.

In this paper, we investigate the task scheduling in resource-limited mobile edge computing (MEC) network, where multiple base stations (BSs), each equipped with a MEC server, assist multiple latency-sensitive user equipments (UEs) in computing. We aim to jointly minimize the system energy consumption and maximize the number of offloaded tasks by optimizing the task scheduling between UEs and BSs. A multiple-objective and mix-integer problem is formulated, which is difficult to solve. To tackle the problem, we combine the ant colony optimization (ACO) algorithm with load balancing, and propose an efficient algorithm. The simulation demonstrates the effectiveness of the proposed algorithm.

关键词： Energy minimization offloading number maximization mobile edge computing load balancing ant colony optimization algorithm

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A Network-Assisted Approach for Ransomware Detection

A Network-Assisted Approach for Ransomware Detection

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作者： Xia, Tianrou PennState University Libraries

学位级别：Master of Science

Ransomware is one kind of malware using cryptography to prevent victims from normal use of their computers. As a result, victims lose the access to their files and desktops unless they pay the ransom to the attackers. By the end of 2019, ransomware attack had caused more than 10 billion dollars of financial loss to enterprises and individuals. In this work, we propose a Network-Assisted Approach (NAA), which contains local detection and network-level detection, to help user determine whether a machine has been infected by ransomware. To evaluate its performance, we built 100 containers in Docker to simulate network scenarios. A hybrid ransomware sample which is close to real-world ransomware is deployed on stimulative infected machines. The experiment results show that our network-level detection mechanisms are separately applicable to WAN and LAN scenarios for ransomware detection.

关键词： Ransomware Detection network security ant colony optimization algorithm

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