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2nd IEEE International Conference on Advanced Computer Control

作者： Zhao Juan-ping Gao Xian-wen Liu Jin-gang Fu Xiu-hui Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781424458479

An improved ant colony algorithm, differential evolution chaos ant colony optimization (DEACO) algorithm, was proposed to plan an optimal collision-free path for mobile robot in 3-D environment. It utilized differential evolution algorithm to update pheromone and chaos disturbance factor was added when pheromone updates for possible stagnation phenomenon. So it enhances escaping capability of algorithm, avoids path-deadlock situations as well as improves the efficiency of planning optimal path. The simulation results indicated that the optimal path on which the robot moves can reach safely and can be rapidly obtained under 3-D space environment, the effect being very satisfactory.

关键词： mobile robot path planning ant colony optimization algorithm differential evolution Chaos sinulation

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Identification and Control based on Neural Networks and ant colony optimization algorithm

Identification and Control based on Neural Networks and Ant ...

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International Conference on Logistics Systems and Intelligent Management

作者： Xu, Qiang Lin, Jihai Yang, Jia Chongqing Technol & Business Univ Coll Comp Sci & Informat Engn Chongqing 400067 Peoples R China Chongqing Univ Technol Coll Elect & Informat Automat Masuho Yamanashi 4000502 Japan

ISBN: (纸本)9781424473281

It is difficult to have good performance to control large delay time system. A neural network identification method for nonlinear system's delay time was discussed. Using the abrupt mutation resulted from the training error sum square of the real output and the expected output of the network, this method changed the input sample period of the neural network so that it could discriminate the delay time of the nonlinear model. Combining the discrimination of neural network system with long time delay and the control method based on model prediction, searching PID controller parameters based on ant colony optimization algorithm, it was applied to control boiler combustion system. The simulation results show that this scheme has much better advantage of celerity and robustness.

关键词： Large Delay Time System Identification of Delay Time Prediction Model ant colony optimization algorithm

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A Hybrid ant colony optimization algorithm for Solving the Ring Arc-Loading Problem

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6th Hellenic Conference on Artificial Intelligence

作者： Bernardino, Anabela Moreira Bernardino, Eugenia Moreira Sanchez-Perez, Juan Manuel Gomez-Pulido, Juan antonio Vega-Rodriguez, Miguel Angel Polytech Inst Leiria Res Ctr Informat & Commun Sch Technol & Management Dept Comp Sci P-2411 Leiria Portugal Univ Extremadura Sch Polytech Dept Technol Comp & Commun Caceres 10071 Spain

ISBN: (纸本)9783642128417

The past two decades have witnessed tremendous research activities in optimization methods for communication networks. One important problem in communication networks is the Weighted Ring Arc-Loading Problem (combinatorial optimization NP-complete problem). This problem arises in engineering and planning of the Resilient Packet Ring (RPR) systems. Specifically, for a given set of non-split and uni-directional point-to-point demands (weights), the objective is to find the routing for each demand (i.e., assignment of the demand to either clockwise or counter-clockwise ring) so that the maximum arc load is minimised. In this paper, we propose a Hybrid ant colony optimization algorithm to solve this problem. We compare our results with the results obtained by the standard Genetic algorithm and Particle Swarm optimization, used in literature.

关键词： Communication Networks optimization algorithms ant colony optimization algorithm Weighted Ring Arc-Loading Problem

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ant colony optimization algorithm Based on Space Division for Container Loading Problem

Ant Colony Optimization Algorithm Based on Space Division fo...

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2010 Chinese Control and Decision Conference

作者： Li Wang~1,Hui Zhang~1,Yan Xiong~2,Dawei Li~2 1.School of Information and Electronic Engineering,University of Science and Technology Liaoning,Anshan,114051,China 2.School of Science,University of Science and Technology Liaoning,Anshan,114051,China

This paper studies container loading optimization *** problem is a subset of rectangular boxes loaded into a rectangular container with fixed dimensions such that maximize container's utilization ratio.A mathematical model is *** principles which include space division,space merger,residual subspace omitted and loading rule are presented.A hybrid algorithm which integrate ant colony optimization algorithm with above principles is used to solve the container loading *** simulation results show that the model and the algorithm are effective.

关键词： Container Loading Problem ant colony optimization algorithm Space Utilization Space Division

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Design of a sensitive air quality monitoring network using an integrated optimization approach

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2016年第3期30卷 779-793页

作者： Benis, Khaled Zoroufchi Fatehifar, Esmaeil Shafiei, Sirous Nahr, Fatemeh Keivani Purfarhadi, Yaser Sahand Univ Technol Environm Engn Res Ctr Fac Chem Engn POB 51335-1995 Tabriz Iran

A new method is developed to design a multi-objective and multi-pollutant sensitive air quality monitoring network (AQMN) for an industrial district. A dispersion model is employed to estimate the ground level concentration of the air pollutants emitted from different emission sources. The primary objective of AQMN is providing the maximum information about the pollutant with respect to (1) maximum coverage area, (2) maximum detection of violations over ambient air standards and (3) sensitivity of monitoring stations to emission sources. ant colony optimization algorithm (ACO) and Genetic algorithm (GA) are adopted as the optimization tools to identify the optimal configuration of the monitoring network. The comparison between the results of ACO and GA shows that the performance of both algorithms is acceptable in finding the optimal configuration of AQMN. The application of the method to a network of existing refinery stacks indicates that three stations are suitable to cover the study area. The sensitivity of the three optimal station locations to emission sources is investigated and a database including the sensitivity of stations to each source is created.

关键词： Air quality monitoring network ant colony optimization algorithm Genetic algorithm Sensitivity of monitoring station

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ant colony optimization algorithm Based on Space Division for Container Loading Problem

Ant Colony Optimization Algorithm Based on Space Division fo...

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The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

作者： Hui Zhang Li Wang Yan Xiong Dawei Li School of Information and Electronic Engineering University of Science and Technology Liaoning Ans School of Science University of Science and Technology Liaoning Anshan 114051 China

This paper studies container loading optimization problem. This problem is a subset of rectangular boxes loaded into a rectangular container with fixed dimensions such that maximize container's utilization ratio. A mathematical model is given. Some principles which include space division, space merger, residual subspace omitted and loading rule are presented. A hybrid algorithm which integrate ant colony optimization algorithm with above principles is used to solve the container loading problem. The simulation results show that the model and the algorithm are effective.

关键词： Container Loading Problem ant colony optimization algorithm Space Utilization Space Division

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Grid Task Scheduling Strategy Based on Particle Swarm optimizationand ant colony optimization algorithm

Grid Task Scheduling Strategy Based on Particle Swarm Optimi...

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International Conference on Advanced Measurement and Test (AMT 2010)

作者： Wei Pengcheng Shi Xi Chongqing Univ Comp Sci & Engn Coll Chongqing 630044 Peoples R China Chongqing Educ Coll Dept Comp Sci Chongqing 630044 Peoples R China

ISBN: (纸本)9780878492695

Based on particle swarm optimization algorithm, this paper presents a grid scheduling optimization algorithm combing the advantages of ant colony optimization algorithm. The algorithm processes task scheduling through particle swarm optimization algorithm to get a group of relatively optimal solutions, and then conducts small-area local search with ant colony optimization algorithm. Theoretical analysis and results of the simulation experiments show that this scheduling algorithm effectively achieves load balancing of resources with comprehensive advantages in time efficiency and solution accuracy compared to the traditional ant colony optimization algorithm and particle swarm optimizationalgorithm, and can be applied to task scheduling in grid computing.

关键词： grid computing task scheduling particle swarm optimization algorithm ant colony optimization algorithm

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An Unsupervised Classification Method of Remote Sensing Images Based on ant colony optimization algorithm

An Unsupervised Classification Method of Remote Sensing Imag...

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6th International Conference on Advanced Data Mining and Applications (ADMA)

作者： Wang, Duo Cheng, Bo Chinese Acad Sci Ctr Earth Observat & Digital Earth Beijing 100864 Peoples R China

ISBN: (纸本)9783642173158

Remote sensing images classification method can be divided into supervised classification and unsupervised classification according to whether there is prior knowledge. Supervised classification is a machine learning procedure for deducing a function from training data;unsupervised classification is a kind of classification which no training sample is available and subdivision of the feature space is achieved by identifying natural groupings present in the images values. As a branch of swarm intelligence, ant colony optimization algorithm has self-organization, adaptation, positive feedback and other intelligent advantages. In this paper, ant colony optimization algorithm is tentatively introduced into unsupervised classification of remote sensing images. A series of experiments are performed with remote sensing data. Comparing with the K-mean and the ISODATA clustering algorithm, the experiment result proves that artificial ant colony optimization algorithm provides a more effective approach to remote sensing images classification.

关键词： unsupervised classification pheromone data discretization ant colony optimization algorithm

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Simultaneous estimation of thermal conductivity and specific heat of thermal protective fabrics using experimental data of high heat flux exposure

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APPLIED THERMAL ENGINEERING 2016年第0期107卷 785-796页

作者： Udayraj Talukdar, Prabal Das, Apurba Alagirusamy, Ramasamy Indian Inst Technol Delhi Dept Mech Engn New Delhi 110016 India Indian Inst Technol Delhi Dept Text Technol New Delhi 110016 India

In the present study, ant colony optimization algorithm is used for an inverse heat transfer problem of parameter estimation. Temperature dependent thermal conductivity and specific heat are estimated simultaneously. Performance of the present algorithm is first analyzed by using the temperature data obtained from the solution of direct problem. Effect of measurement error on the property estimation is then analyzed. Accuracy of estimated properties is found to be good in both the cases. Experiments are then conducted on a cotton duck fabric exposed to 40 kW/m(2) radiant heat flux using a bench top test according to ISO 6942. Temporal variation of sensor temperature is used to estimate the thermal conductivity and specific heat of the fabric sample. The estimated values are in agreement with the results available in the literature. Property values estimated in the study are included in a coupled conduction-radiation heat transfer model for fabrics. Performance of the fabric sample is further analyzed with the numerical model for high heat fluxes. The simulation results compare quite well with experimental data at 80 kW/m(2) radiant heat and 70 kW/m(2) flame exposures. Flame exposure experiment is conducted according to ISO 9151. Good agreement between the numerical and experimental results are found for both the exposures. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Coupled conduction-radiation Parameter estimation ant colony optimization algorithm High heat exposure Thermal protective fabric Thermal conductivity Specific heat

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The Production Scheduling Model of Optical Components Oriented ant colony optimization algorithm

The Production Scheduling Model of Optical Components Orient...

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5th International Symposium on Advanced Optical Manufacturing and Testing Technologies - Smart Structures and Materials in Manufacturing and Testing

作者： Wang Juan Wang Jian Tang Dingyong China Acad Engn Phys Inst Comp Applicat Mianyang Peoples R China

ISBN: (纸本)9780819480897

Optical components production belongs to the typical jobbing work, and its scheduling problem is a significant research in advanced optical manufacture technique. According to the manufacturing technology for high precision optical components, the production scheduling model which is combined workshop scheduling theory with improved ant colony algorithm (ACA) is introduced in this paper. In order to reduce the inherent deficiency of traditional ant algorithm for local optimal solution and stagnation, an improved algorithm which can simulate real ant colony sensation and consciousness may enhance the efficiency of basic ACA. The simulation experiment shows the robustness for this improved algorithm.

关键词： production scheduling ant colony optimization algorithm sensation and consciousness

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