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Task optimization and scheduling of distributed cyber-physical system based on improved ant colony algorithm

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 109卷 134-148页

作者： Yi, Na Xu, Jianjun Yan, Limei Huang, Lin Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intelligen Daqing 163318 Heilongjiang Peoples R China

Cyber-physical system (CPS) is the product of technological development to a certain stage, and also is the future trends in information technology. High-performance computing ability is the guarantee of CPS's real-time and accuracy applications, and the emergence of distributed technology provides the implementation possibility of high-performance CPS. Task scheduling is a typical combination optimization problem and the task allocation problem on multi-processor distributed systems refers to how to use system resources most efficiently in a distributed computing environment to complete a limited set of tasks. Based on the behavior of ants searching for food in nature, ant colony algorithm is a kind of positive feedback algorithm with good robustness and easy parallel implementation and has certain advantages for dealing with constraint satisfaction. In order to introduce an adaptive mechanism and mutation strategy, shorten the calculation time of ant colony algorithm, speed up CPS algorithm convergences, and improve distributed CPS prediction accuracy, this paper analyzed the research status and significance of ant colony algorithm, expounded the development background, current situation, and future challenges of task optimization and scheduling of distributed CPS, elaborated the principles and methods of ant colony optimization algorithm model and mathematical description of CPS task scheduling, proposed a task management model of distributed CPS based on improved ant colony algorithm, explored the task optimization scheduling of distributed CPS based on improved ant colony algorithm, and finally conducted an numerical simulation to test the effect the proposed algorithm and model. The simulation results show that the proposed algorithm model enhances the local search ability and improves the quality of the task scheduling problem, and has good effectiveness, stability and adaptability. The study results of this paper provide a reference for the further research

关键词： Cyber-physical system (CPS) Task scheduling ant colony algorithm Distributed system

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An ant colony algorithm for efficient ship routing

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POLISH MARITIME RESEARCH 2013年第3期20卷 28-38页

作者： Tsou, Ming-Cheng Cheng, Hung-Chih Natl Kaohsiung Marine Univ Dept Shipping Technol Kaohsiung Taiwan Natl Sun Yat Sen Univ Dept Marine Environm & Engn Kaohsiung 80424 Taiwan

With the substantial rising of international oil price and global warming on the rise, how to reduce operational fuel consumption and decrease air pollution has become one of the pursued goals of green ship. Ship route planning is an indispensible part of the ship navigation process, especially in transoceanic crossing ship routing. The soundness of ship routing not only affects the safety of ship navigation but also the operation economy and environmental protection. This research is based on the platform of Electronic Chart Display and Information System (ECDIS), and founded on ant colony algorithm (ACA) combined with the concept of Genetic algorithm (GA), to model living organisms optimization behaviour to perform efficient ship route planning in transoceanic crossing. Besides the realization of route planning automation, ship routing will achieve the goal of optimum carbon dioxide reduction and energy conservation, and provide reference for route planning decision.

关键词： ship routing ant colony algorithm GIS weather routing

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The new treatment mode research of hepatitis B based on ant colony algorithm

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2021年第4期42卷 740-759页

作者： Yu, Jing Xing, Lining Tan, Xu Natl Univ Def Technol Coll Syst Engn Changsha 400073 Hunan Peoples R China ShenZhen Inst Informat Technol Sch Software Engn Shenzhen 518172 Peoples R China

Hepatitis B (HB) is a deadly disease that has a severe impact on infected individuals. In China, not only are the incidence and infection rates of HB very high, but also many HB patients suffer from mental illness associated with anxiety and fear because of HB-associated symptoms. This exacerbates the patients' condition, potentially increasing the risk of mortality. In this paper, we propose a new treatment mode to improve the therapeutic efficiency and patients' satisfaction with their healthcare. In a single process of this new treatment, several patients with similar disease symptoms are treated by one doctor at the same time. This new treatment mode can not only relieve the anxiety and fear of HB patients, and improve patients' cognition rate of HB, but also reduce the HB infection rate, slow down the progression of disease symptoms, and shorten the course. If patients with similar disease symptoms are to be grouped together, there is a need to determine the optimal patient batch combination, which can be solved in the new mode, called patient combined problem (PCP). We also constructed a mathematical model of PCP, and present the ant colony (AC) algorithm and Enhanced AC with a P-3-exchange operator for PCP in the new treatment mode in this paper. We also performed an experiment that showed that our proposed algorithms are very fast and effective for solving this problem.

关键词： Hepatitis B Combinatorial optimization ant colony algorithm Patient satisfaction New treatment mode Local search

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Wireless sensor network routing method based on improved ant colony algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第3期10卷 991-998页

作者： Zou, Zongfeng Qian, Ying Shanghai Univ Sch Management 99 Shangda Rd Shanghai 200444 Peoples R China

Wireless sensor networks (WSNs) are widely applied in smart manufacturing because their installation does not need fixed infrastructure and can be used where cabling and power supply are difficult. Given the limited energy supply and computing capability of a WSN, an efficient routing algorithm for data transmission is essential for its performance. ant colony optimization is used in WSNs to identify shortest paths, and thus reduce the energy consumption of the network. However, ant colony optimization is prone to falling into local optima and convergences slowly. We hence propose an improved ant colony algorithm that can be used to construct the sensor node transfer function and pheromone update rule, and adaptively choose a data route by adopting the advantages of the dynamic state of the network. The simulation results show that the proposed method can further reduce energy consumption, time delay, and data packet losses. Thus, the quality of service of the WSN is improved by its use.

关键词： Wireless sensor networks ant colony algorithm Energy balance Quality of service

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Design of wind farm layout using ant colony algorithm

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RENEWABLE ENERGY 2012年 44卷 53-62页

作者： Eroglu, Yunus Seckiner, Serap Ulusam Gaziantep Univ Dept Ind Engn Fac Engn TR-27310 Gaziantep Turkey

The wind is a clean, abundant and entirely renewable source of energy. Large wind farms are being built around the world as a cleaner way to generate electricity, but operators are still searching for more efficient wind farm layouts to maximize wind energy capture. This paper presents an ant colony algorithm for maximizing the expected energy output. The algorithm considers wake loss, which can be calculated based on wind turbine locations, and wind direction. The proposed model is illustrated with three different scenarios of the wind speed and its direction distribution of the windy site and, comparing to evolutionary strategy algorithm available in literature. The results show that the ant colony algorithm performs better than existing strategy, in terms of maximum values of expected energy output and wake loss. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Wind farm Wind turbine Layout design Optimization ant colony algorithm Renewable energy

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Fuzzy scheduling optimization system for multi-objective transportation path based on ant colony algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第4期35卷 4257-4266页

作者： Wu, Gengrui Bo, Niao Wu, Husheng Yang, Yong Hassan, Nasruddin Armed Police Force Engn Univ Mat Management & Support Coll Xian Shaanxi Peoples R China Police Officer Coll Armed Police Force Basic Dept Chengdu Sichuan Peoples R China Police Officer Coll Armed Police Force Dept Informat & Commun Chengdu Sichuan Peoples R China Univ Kebangsaan Malaysia Sch Math Sci Fac Sci & Technol Ukm Bangi Selangor Malaysia

The key algorithm of the traditional system is aimed at the minimum of a certain factor, but does not consider the uncertain conditions and various modes of transportation, and the result of the scheduling is not excellent. To this end, a new fuzzy scheduling optimization system based on ant colony algorithm for multi-objective transportation path is designed. Based on the GPS module, a fuzzy scheduling optimization system based on ant colony algorithm for multi-objective transportation path is designed, and the overall structure of the system is given. The scheduling optimization problem of freight transport lines is described, and the volume of demand, the total volume of delivery and the remaining number of vehicles are made fuzzy processing. The goal is to minimize the total time of the advance or tardiness of the transportation and the total cost, so that the fuzzy scheduling model of transportation path is built. According to the principle of ant colony algorithm, the built multi-objective model will be transformed into a single objective model, and combined with the objective function, the index heuristic information and the performance of ant colony algorithm are set, and the optimal solution of that the deviation is minimum with the ideal solution is calculated by using ant colony algorithm, so as to achieve the multi-objective transportation path scheduling. The experimental results show that the total transportation distance of the designed system is short, the total cost is low, and the goods can be delivered in time.

关键词： ant colony algorithm multi-objective transportation path fuzzy scheduling

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An Improved ant colony algorithm for Optimized Band Selection of Hyperspectral Remotely Sensed Imagery

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IEEE ACCESS 2020年 8卷 25789-25799页

作者： Ding, Xiaohui Li, Huapeng Yang, Ji Dale, Patricia Chen, Xiangcong Jiang, Chunlei Zhang, Shuqing Guangzhou Inst Geog Guangzhou 510070 Peoples R China Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130102 Peoples R China Southern Marine Sci & Engn Guangdong Lab Guangzho Guangzhou 511458 Peoples R China Guangdong Open Lab Geospatial Informat Technol & Guangzhou 510070 Peoples R China Key Lab Guangdong Utilizat Remote Sensing & Geog Guangzhou 510070 Peoples R China Griffth Univ Sch Environm Environm Futures Res Inst Brisbane Qld 4122 Australia Harbin Inst Geotech Invest & Surveying Harbin 150010 Peoples R China Jilin Jianzhu Univ Sch Geomat & Prospecting Engn Changchun 130114 Peoples R China

The ant colony algorithm (ACA) has been widely used for reducing the dimensionality of hyperspectral remote sensing imagery. However, the ACA suffers from problems of slow convergence and of local optima (caused by loss of population diversity). This paper proposes an improved ant colony algorithm (IMACA) based band selection algorithm (IMACA-BS), to overcome the two shortcomings of the standard ACA. For the former problem, a pre-filter is applied to improve the heuristic desirability of the ant colony system;the Pearson;s similarity measurement of the degree of redundancy among the selected bands is taken as one of the terms in the heuristic function, and this further accelerates the convergence of the IMACA-BS. For the latter problem, a pseudo-random rule and an adaptive information update strategy are, respectively, introduced to increase the population diversity of the ant colony system. The effectiveness of the proposed algorithm was evaluated on three public datasets (Indian Pines, Pavia University and Botswana datasets), and compared with a series of benchmarks. Experimental results demonstrated that the IMACA-BS consistently achieved the highest overall classification accuracies and significantly outperformed other benchmarks over all of the three experiments. The proposed IMACA-BS is, therefore, recommended as an effective alternative for band selection of hyperspectral imagery.

关键词： Hyperspectral remotely sensed imagery band selection ant colony algorithm artificial intelligence

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Microrobot based micro-assembly sequence planning with hybrid ant colony algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2008年第11-12期38卷 1227-1235页

作者： Shuang, Bing Chen, Jiapin Li, Zhenbo Shanghai Jiao Tong Univ Inst Micro & Nano Sci & Technol Natl Key Lab Nano Micro Fabricat Technol Key Lab Thin Film & MicrofabricatMinist Educ Shanghai 200030 Peoples R China

Robot-based assembly sequence planning plays an important role in product design and has been widely researched in the macro world. But in the micro world, the characteristics of microrobot-based assembly, such as complexity and scaling effects, make the assembly problems much more difficult and seldom researched. In this paper, the microrobot-based micro-assembly sequence planning problem is discussed. The problem is transferred as a combinatorial optimization problem with several matrixes, such as the moving wedge matrix, the microrobot performance matrix, and the sensing matrix. Furthermore, the geometrical and visibility constraints of assembly sequence and evaluation criteria for optimization are given. A particle swarm optimization (PSO) algorithm modified ant colony optimization (ACO) algorithm, called a hybrid PS-ACO, is devised to solve the problem efficiently. The combination of local search and global search of PSO is introduced into the ACO algorithm, which can balance the exploration and exploitation performances of searches. The experimental results have shown that the PS-ACO can solve the micro-assembly sequence planning problem with better convergence performance and optimizing efficiency than basic ACO and GA.

关键词： microrobot micro-assembly assembly sequence planning ant colony algorithm

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Link Design for Wireless Optical Communication Network based on ant colony algorithm

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JOURNAL OF COMMUNICATIONS AND NETWORKS 2022年第2期24卷 184-191页

作者： Wang, Xiaorui Chen, Dongdong Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou Peoples R China

the maturation of space laser communication technologies and the development of deep-space exploration technologies, deep-space optical network (DSON) is expected to become an important foundation and inevitable development trend of future deep-space communication. According to the characteristics of DSON, we present a DSON topology structure based on flat wireless mesh network (FWMN). To improve the robustness of DSON and realize load balancing, the Routing algorithm based on ant colony algorithm (RA-ACA) for DSON is proposed in this paper. We primarily research the unidirectional links problem and node energy consumption of DSON with RA-ACA. Instead of deleting the unidirectional links in the whole networks used in traditional ant colony algorithm (TACA), we make the best of unidirectional links, and then the performance of network will be improved. Also, in order to control the energy consumption speed of nodes and prevent premature death of nodes, we put the node energy as the routing factor in the routing, thus the lifetime of the whole network will be increased. Finally, aiming to enhance the convergence speed, we reserve the node pheromone for the subsequent path finding. Simulation results demonstrate the advantages of the proposed RA-ACA compared with TACA for DSON.

关键词： ant colony algorithm deep-space optical net-work (DSON) node energy undirectional links

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Multi-Population ant colony algorithm for Virtual Machine Deployment

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IEEE ACCESS 2017年 5卷 27014-27022页

作者： Sun, Xuemei Zhang, Kai Ma, Maode Su, Hua Tianjin Polytech Univ Sch Comp Sci & Software Tianjin 300387 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

With the recent rapid development of cloud computing technology, how to reduce the costs of a cloud data center effectively has become an important issue. The study on virtual machine deployment mainly aims at deploying virtual machine resources required by users on a physical server rationally and effectively. This paper proposes a multi-population ant colony algorithm to solve problems of virtual machine deployment. With resource wastage and energy consumption as optimization objectives, this algorithm uses multiple ant colonies for the solution and determines strategies for information exchange among ant colonies according to the information entropy of each population to guarantee the balance of its convergence and diversity. The simulation results show that this algorithm has better performance than the single-population ant colony algorithm and can reduce resource wastage and energy consumption effectively for high-demand virtual machine deployment.

关键词： Multi-population ant colony algorithm cloud computing virtual machine

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