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检索条件"主题词=Ant Colony Optimization algorithm"

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An ant colony optimization algorithm based on Scheduling Preference for Maximizing Working Time of Wireless Sensor Networks 16

An Ant Colony Optimization Algorithm based on Scheduling Pre...

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17th Genetic and Evolutionary Computation Conference (GECCO)

作者： Liu, Yu Chen, Wei-Neng Hu, Xiao-Min Zhang, Jun Sun Yat Sen Univ Sch Adv Comp Guangzhou 510275 Guangdong Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China Collaborat Innovat Ctr High Performance Comp Beijing Peoples R China Minist Educ Engn Res Ctr Supercomp Engn Software Beijing Peoples R China Sun Yat Sen Univ Sch Publ Hlth Guangzhou 510275 Guangdong Peoples R China

ISBN: (纸本)9781450334723

With the proliferation of wireless sensor networks (WSN), the issues about how to schedule all the sensors in order to maximize the system's working time have been in the spotlight. Inspired by the promising performance of ant colony optimization (ACO) in solving combinational optimization problem, we attempt to apply it in prolonging the life time of WSN. In this paper, we propose an improved version of ACO algorithm to get solutions about selecting exact sensors to accomplish the covering task in a reasonable way to preserve more energy to maintain longer active time. The methodology is based on maximizing the disjoint subsets of sensors, in other words, in every time interval, choosing which sensor to sustain active state must be rational in certain extent. With the aid of pheromone and heuristic information, a better solution can be constructed in which pheromone denotes the previous scheduling experience, while heuristic information reflects the desirable device assignment. Orderly sensor selection is designed to construct an advisable subset for coverage task. The proposed method has been successfully applied in solving limited energy assignment problem no matter in homogenous or heterogeneous WSNs. Simulation experiments have shown it has a good performance in addressing relevant issues.

关键词： ant colony optimization algorithm Wireless Sensors Network (WSN) maximize working time schedule

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Research on Routing algorithm for Time-Sensitive Networks Based on ant colony optimization algorithm 6

Research on Routing Algorithm for Time-Sensitive Networks Ba...

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6th International Conference on Natural Language Processing (ICNLP)

作者： Yang, Yanzhu Yang, Wujun Chang, Zhixian Xian Univ Posts & Telecommun Dept Sch Commun & Informat Engn Xian Peoples R China

ISBN: (纸本)9798350349122;9798350349115

Time-Sensitive Network (TSN) is a type of deterministic Ethernet that can satisfy the need for deterministic transmission of data in many industries. TSN provides bounded delay for Time Triggered (TT) communication by providing global scheduling of transmission messages. Therefore, to address the problem of delay optimization for path selection of TT flows in industrial TSN, this paper puts forward a kind of oriented industrial TT flows routing multipath time delay difference of perception of transport mechanism. A multipath forwarding metric is designed based on the sending delay and processing delay, and then the multipath forwarding problem is transformed into a 0-1 Integer Linear Programming (ILP) problem. Finally, based on the ant colony optimization (ACO) algorithm, the refined algorithm (RACO) offers reliable deterministic end-to-end delay guarantees for TT flows in the industrial Internet. Through a large number of simulation experiments and analysis of the experimental results, this scheme realizes the low-latency parallel transmission of flows and achieves lower end-to-end delay, with faster convergence speed and stronger global optimization capability, which can more effectively avoid the possibility of falling into a stagnant state.

关键词： time-sensitive network time-triggered flow path selection strategy ant colony optimization algorithm

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Research of Path Planning for Mobile Robot based on Improved ant colony optimization algorithm

Research of Path Planning for Mobile Robot based on Improved...

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2nd IEEE International Conference on Advanced Computer Control

作者： Zhao Juan-ping Liu Jin-gang Univ Chem Technol Coll Informat Engn Shenyang Shenyang Peoples R China Northeastern Univ Coll Informat Sci & engn Shenyang Peoples R China Shenyang Xinghua Aero Elect Appliances Co Ltd Lia Shenyang Peoples R China

ISBN: (纸本)9781424458479

The paper proposes an improved ant colony optimization algorithm. This method first designs two fuzzy controllers to optimize three parameters alpha, beta, rho. Then it establishes a dynamic searching window for ants and chaos information are added when near-neighbour city table is constituted in order to increase research speed in initial stages of algorithm. In addition, the concept of active degree of city node is presented as future information to supervise ants to construct solution and update pheromone. Finally a new evaluation criterion is produced to distinguish where paths are excellent or not. So the strategy not only conquers the weakness of easily running into local optimization while making route optimization, but also enhances efficient convergence of ant colony optimization algorithm. Results of large numbers of computer simulations demonstrate that this novel algorithm can plan optimal path rapidly in intricate three dimension (3-D) environment.

关键词： Mobile Robot Path Planning ant colony optimization algorithm Fuzzy Control Dynamic Window Parameters optimization

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Practice System of ant colony optimization algorithm in Business Administration 3rd

Practice System of Ant Colony Optimization Algorithm in Busi...

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International Workshop on New Approaches for Multidimensional Signal Processing (NAMSP)

作者： Ma, Deyong Wang, Jun Hainan Univ Haikou Hainan Peoples R China

ISBN: (纸本)9789811978449;9789811978425;9789811978418

AS a result of routine businessmanagement in the practical application of such problems as low efficiency, the content is not fine, so if you want to optimize the effect of market regulation, improve business management efficiency, and ensure quality of market operation, need to be in the original content on the basis of reasonable use of information technology management idea, this is also discusses the main problems of the current market industry. Based on the application of particle swarm optimization (PSO), this paper integrates it with ant colony algorithm, and then uses the whole process of fine management mode to carry out visual and cyclic supervision and scheduling of the whole market inspection process. The results of this study can improve actual work efficiency, strengthen industrial and commercial scheduling management, and reduce the work pressure faced by industrial and commercial personnel. It is thework content of enterprise management that is more standardized.

关键词： ant colony optimization algorithm Particle Swarm optimization Business Administration Market Regulation

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Passenger Capacity Prediction Based on Least Squares Support Vector Regression with Continuous ant colony optimization algorithm

Passenger Capacity Prediction Based on Least Squares Support...

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International Conference on Information System and Artificial Intelligence (ISAI)

作者： Li, Dong-Ping Kunming Univ Inst Informat Sci & Technol Kunming 650214 Peoples R China

ISBN: (纸本)9781509015856

In order to solve the shortcoming of support vector regression, passenger capacity prediction based on least squares support vector regression with ant colony optimization algorithm is proposed in this paper. ant colony optimization algorithm is used to select the parameters of least squares support vector regression. Highway passenger capacity data of Anhui province from 2000 to 2011 are applied to testify the passenger capacity forecasting performance of ACO-LSSVR, among which the passenger capacity data from 2000 to 2009 are used to create the training samples and the passenger capacity from 2010 to 2011 are used as the testing samples. The testing results show that the forecasting ability for highway passenger capacity of ACO-LSSVR is better than those of LSSVR and SVR.

关键词： Highway passenger capacity Prediction technology ant colony optimization algorithm Least squares support vector regression

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Implementation of Robotic Path Planning using ant colony optimization algorithm

Implementation of Robotic Path Planning using Ant Colony Opt...

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IEEE International Conference on Inventive Computation Technologies (ICICT)

作者： Joshy, Parvathy Supriya, P. Amrita Univ Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Elect Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781509012855

Mobile robot path planning is critical in the present day of automation. Several situations may occur for humans, like the environment may be dirty, hazardous, might cause death, or injury as in case of mining, detecting leakage in pipe, cleaning of pipe etc. where robots can be successfully employed. The idea of this paper is to develop a mobile robot that finds the shortest route from source to destination by using ant colony optimization algorithm with a single robot. The hardware used is iRobot Create interfaced to NXP LPC1768 Cortex M3 controller. The same is simulated using MATLAB. The output of the hardware is also made visible in Teraterm. It is observed that the mobile robot is enhanced with considerable skills to trace a path of optimum distance from source to destination without any collision in most of the situations, barring a few.

关键词： ant colony optimization algorithm Pheromones

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A Logistic Distribution Routes Solving Strategy Based on the Physarum Network and ant colony optimization algorithm 17

A Logistic Distribution Routes Solving Strategy Based on the...

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2015 IEEE 17th International Conference on High Performance Computing and Communications (HPCC)

作者： Chen, Qianguo Qian, Tao Liu, Kui Chongqing Technol & Business Univ Ctr Teaching & Learning Dev Chongqing Peoples R China Bank Chongqing Software Dev Ctr Chongqing Peoples R China Southwest Univ Expt Teaching Ctr Int Studies Chongqing Peoples R China

ISBN: (纸本)9781479989379

The Physarum Network with single inlet and multi outlet model (SMPN) exhibits a unique feature that the critical pipelines are reserved with the evolution of network. In addition, ant colony optimization algorithm is a classic optimization algorithm of simulated evolutionary algorithms, which has been used to solve optimal scheduling problems. In this paper, drawing on this feature, an optimized ant colony optimization (ACO) algorithm denoted as SMPNACO algorithm is proposed based on the Physarum Network and ant colony optimization algorithm (ACO) to solve the Vehicle Routing Problem (VRP). Throughout the algorithm, the amount of pheromone flowed in network are related to the customers' requirement. When the pheromone matrix is updated, the SMPNACO algorithm updates both the pheromone released by ants and the flowing pheromone in the Physarum Network. By adding extra pheromones in the Physarum Network improves the convergence performance of ant colony optimization algorithm. The simulative experiments show that the SMPNACO algorithm is less affected by the initial total pheromone, this algorithm is feasible in solving the small scale VRP, and can effectively solve the VRP.

关键词： Physarum Network model ant colony optimization algorithm Logistics distribution routes optimization VRP

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A Task Scheduling algorithm Based on Genetic algorithm and ant colony optimization algorithm with Multi-QoS Constraints in Cloud Computing 7

A Task Scheduling Algorithm Based on Genetic Algorithm and A...

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7th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Dai, Yangyang Lou, Yuansheng Lu, Xin Hohai Univ Coll Comp & Informat Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479986460

Task scheduling problem in cloud computing environment is NP-hard problem, which is difficult to obtain exact optimal solution and is suitable for using intelligent optimization algorithms to approximate the optimal solution. Meanwhile, quality of service (QoS) is an important indicator to measure the performance of task scheduling. In this paper, a novel task scheduling algorithm MQoS-GAAC with multi-QoS constraints is proposed, considering the time-consuming, expenditure, security and reliability in the scheduling process. The algorithm integrates ant colony optimization algorithm (ACO) with genetic algorithm (GA). To generate the initial pheromone efficiently for ACO, GA is invoked. With the designed fitness function, 4-dimensional QoS objectives are evaluated. Then, ACO is utilized to seek out the optimum resource. The experiment indicates that the proposed algorithm has preferable performance both in balancing resources and guaranteeing QoS.

关键词： task scheduling cloud computing genetic algorithm ant colony optimization algorithm QoS

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Search Technology of Resource Service in Manufacturing Grid Based On ant colony optimization algorithm

Search Technology of Resource Service in Manufacturing Grid ...

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International Conference on Applied Mechanics and Mechanical Engineering

作者： Lou, Jing Xu, Hongxiang Jiangsu Teachers Univ Technol Sch Comp Engn Changzhou Peoples R China Jiangsu Teachers Univ Technol Sch Mech & Automobile Engn Changzhou Peoples R China

ISBN: (纸本)9780878492459

In Manufacturing Grid (MG) system, there are primarily two kinds of users: resource service consumer and resource service provider. For a resource service request task, the MG system should search the services which are qualified for the requirements of consumer and then choose the optimal one to execute it. In according to the distributed system structure of MG and node routing model influenced by several factors (e.g., time minimization, cost minimization, bandwidth minimization), the searching technology based on web service technology and ant colony optimization (ACO) algorithm has been proposed in this paper. The basic evaluation models and formulations are described, and then the algorithms are presented to minimize the expenses. The experimental results also show that the proposed method is useful in solving the searching problems in MG system.

关键词： manufacturing grid resource service resource searching ant colony optimization algorithm

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Application of the ant colony optimization algorithm for Reconstruction of the Thermal Conductivity Coefficient

Application of the Ant Colony Optimization Algorithm for Rec...

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International Symposium on Swarm and Evolutionary Computation/ Symposium on Swarm Intelligence and Differential Evolution

作者： Hetmaniok, Edyta Slota, Damian Zielonka, Adam Silesian Tech Univ Inst Math PL-44100 Gliwice Poland

ISBN: (纸本)9783642293528;9783642293535

In this paper the parametric inverse heat conduction problem with the third kind boundary condition is solved by applying the ant colony optimization algorithm introduced in recent years and belonging to the group of optimization algorithms inspired by the behavior of swarms of individuals living in real word. In this case the applied algorithm is based on the technique of searching for the shortest way connecting the ant-hill with the source of food and is used for minimizing the functional playing a crucial role in the proposed procedure prepared for reconstruction of the thermal conductivity coefficient.

关键词： Artificial Intelligence Swarm Intelligence ant colony optimization algorithm Inverse Heat Conduction Problem.

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