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International Conference on Intelligent Computing, Automation and Systems (ICICAS)

作者： Guoliang Liu Xinhua Wang Bing Liu Chunyan Wei Jiahuan Li Science and Technology on Avionics Integration Laboratory Shanghai China Nanjing University of Aeronautics and Astronautics Automation College Nanjing China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9781728161068

ISBN: (纸本)9781728161075

Multiple unmanned aerial vehicles (UAVs) formation has a broad application prospects, which will be used widely in the civilian and military fields. An improved ant colony algorithm method is proposed to solve the problem of path planning in formation transformation. Firstly, we designed a location assignment based on ant colony optimization algorithm. Then a new node selection strategy is proposed to improve the optimization results. At the end, the simulation results show the effectiveness and progress of the method. The improved method proposed in this paper can avoid the results into the local optimization and the algorithm optimization speed has increased.

关键词： ant colony optimisation autonomous aerial vehicles path planning remotely operated vehicles Path planning Unmanned vehicles ant algorithm ant colony optimization Unmanned Aerial Vehicle local optimization Simulation results

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Traffic Signal Optimization Using ant Colony algorithm

Traffic Signal Optimization Using Ant Colony Algorithm

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International Joint Conference on Neural Networks

作者： David Renfrew Xiao-Hua Yu Department of Electrical Engineering California Polytechnic State University

ISBN: (纸本)9781467314886

Traffic signal control is an effective way to improve the efficiency of traffic networks and reduce users' delays. ant Colony Optimization (ACO) is a meta-heuristic algorithm based on the behavior of ant colonies searching for food. ACO has successfully been employed to solve many complicated combinatorial optimization problems and its stochastic and decentralized nature fits well with traffic networks. This research investigates the application of the ant colony algorithm to minimize user delay at traffic intersections. Various ACO algorithms are discussed and a rolling horizon approach is also employed to achieve real-time adaptive control. Computer simulation results show that this new approach outperforms conventional fully actuated control, especially under the condition of high traffic demand.

关键词： ant colony algorithm Traffic signal control Optimization ant algorithm Traffic signals ant colony optimization ADMINISTRATIVE CONSENT ORDER traffic network Travel demand Traffic Stochastic

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Comparative Analysis of Genetic algorithm and ant Colony algorithm on Solving Traveling Salesman Problem

Comparative Analysis of Genetic Algorithm and Ant Colony Alg...

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IEEE International Workshop on Semantic Computing and Systems, WSCS

作者： Kangshun Li Lanlan Kang Wensheng Zhang Bing Li School of Information Engineering Jiangxi University of Science and Technology China Institute of Automation Chinese Academy and Sciences China State Key Laboratory of Engineering Software Wuhan University of China China

ISBN: (纸本)9780769533162

ant Colony algorithm and Genetic algorithm (GA), two bionic-inspired optimization algorithms, have great potentials to solve the combination optimization problems, respectively used in solving traveling salesman problem, but there are some shortcomings if only one of them is used to solve TSP. Performance comparative analysis have been done by using ACA and GA respectively in solving TSP in this paper. The experiments show the advantages and disadvantages used only ACA or GA, we can overcome the shortcomings if GA and ACA are combined to solve TSP and get faster convergent speed and more accurate results compared with only using ACA or GA.

关键词： travelling salesman problems ant algorithm Gallium Genetic algorithm (GA) Optimization algorithms Solve

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Research on parameters optimization and simulation of the ant Colony algorithm

Research on parameters optimization and simulation of the An...

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International Conference on Cyberspace Technology (CCT 2014)

作者： Xing Wei Zhiyuan Li Jingjing Qu Guilin University of Aerospace Technology China

ISBN: (纸本)9781510802704

Parameters optimization are studied to improve the speed of computing in the ant Colony algorithm, Preferences unreasonable takes a long time and search is slow. In order to improve search efficiency of the algorithm, Parameter optimization rules are proposed in the ant Colony algorithm, The algorithm improves the basic ant colony algorithm in the parameter selection rules of α.β.ρ. and m, by improving, the ant colony effectively avoid falling into local optimum, speed up the convergence and improve the search efficiency. Simulation results show that parameters optimization effectively improve the performance of the algorithm, the works are feasible and beneficial to the application and development of the ant colony algorithm.

关键词： ACA Parameters optimization optimization Parameter optimization ant algorithm

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Spatial Straightness Error Evaluation with an ant Colony algorithm

Spatial Straightness Error Evaluation with an Ant Colony Alg...

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International Conference on Granular Computing

作者： Ke Zhang School of Mechanical and Automation Engineering Shanghai Institute of Technology

ISBN: (纸本)9781424425129

Based on the analysis of existent evaluation methods for spatial straightness errors, an intelligent evaluation method is provided in this paper. The evolutional optimum model and the calculation process are introduced in detail. According to characteristics of spatial straightness error evaluation, ant colony optimization (ACO) algorithm is proposed to evaluate the minimum zone error. Compared with conventional optimum evaluation methods such as simplex search and Powell method, it can find the global optimal solution, and the precision of calculating result is very high. Then, the objective function calculation approaches for using the ACO to evaluate minimum zone error are formulated. Finally, the control experiment results evaluated by different optimal methods such as the least square, simplex search, Powell search and Genetic algorithm, indicate that the proposed method does provide better accuracy on spatial straightness error evaluation, and it has fast convergent speed as well as using computer and popularizing application easily.

关键词： ant algorithm evaluation method computational procedure ADMINISTRATIVE CONSENT ORDER Powell method Simplex space-straightness Genetic algorithm (GA)

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The Application of ant Colony algorithm and Artificial Neural Network in Tax Assessment

The Application of Ant Colony Algorithm and Artificial Neura...

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2010 International Conference on Audio,Language and Image Processing(2010年音频、语言与图像处理国际会议 ICALIP 2010)

作者： Shuiling Mao Wanggen Wan Rui Wang Yue Gao School of Communication and Information Engineering Shanghai University Shanghai P.R.China

ISBN: (纸本)9781424458653

Tax assessment is an important and complex task in tax administration. A large number of data is involved in the process. Hence, a scientific model is in demanding. In this paper, we present a model which integrates the ant colony algorithm into artificial neural network to improve the performance of neural network in judgment of whether the taxpayer is credible. In details, we use ant colony algorithm to train the weights of artificial neural network, and this method could avoid some defects of artificial neural network. The simulation result validates the effectiveness of our method.

关键词： ant algorithm Artificial neural networks tax Simulation results Judgment

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A Path Planning Method of Robot Arm Obstacle Avoidance Based on Dynamic Recursive ant Colony algorithm

A Path Planning Method of Robot Arm Obstacle Avoidance Based...

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IEEE International Conference on Power, Intelligent Computing and Systems

作者： Zhao Huadong Lei Chaofan Jiang Nan School of Mechanical Engineering Zhengzhou University

ISBN: (纸本)9781728137216

In order to improve the efficiency and accuracy of obstacle avoidance path planning, the characteristics of obstacle avoidance path planning are analyzed. Based on the traditional ant colony algorithm, sliding window and forgetting factor are introduced. By adjusting the correlation parameters and pheromone rules, the fixed value ant colony parameters cannot meet the performance of the whole calculation process. Through the cooperation of ants, an optimal path was established to avoid obstacles. An optimal moving path of manipulator based on dynamic recursive ant colony algorithm was proposed, and the practicability and validity of the method were verified by an example. It provides a reference for finding the optimal solution to the manipulator path planning in the shortest time.

关键词： ant colony algorithm Robot arm Obstacle avoidance path planning ant algorithm Path planning robotic arm Obstacle avoidance Collision avoidance correlation parameter computational procedure optimal solution Recursive

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ant algorithms for image feature extraction

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第11期40卷 4315-4332页

作者： Mullen, Rob J. Monekosso, Dorothy N. Remagnino, Paolo Univ Kingston Robot Vis Team Kingston Upon Thames KT1 2EE Surrey England

This paper extends on previous work in applying an ant algorithm to image feature extraction, focusing on edge pattern extraction, as well as the broader study of self-organisation mechanisms in digital image environments. A novel method of distributed adaptive thresholding is introduced to the ant algorithm, which enables automated distributed adaptive thresholding across the swarm. This technique is shown to increase performance of the algorithm, and furthermore, eliminates the requirement for a user set threshold, allowing the algorithm to autonomously adapt an appropriate threshold for a given image, or data set. Additionally this approach is extended to allow for simultaneous multiple-swarm multiple-feature extraction, as well as dynamic adaptation to changing imagery. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： ant algorithm Swarm intelligence Feature extraction Image processing

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ant colony optimisation algorithm for detection and tracking of non-structured roads

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ELECTRONICS LETTERS 2008年第12期44卷 725-726页

作者： Arnay, R. Acosta, L. Sigut, M. Toledo, J. Univ La Laguna Dept Syst Engn & Automat San Cristobal la Laguna 38204 Santa Cruz Tene Spain

An ant colony optimisation approach is proposed to address the problem of non-structured roads real-time detection and tracking by a self-guided electrical vehicle. The solution adopted tries to find some properties describing the probability that a pixel belongs to the boundaries of the road, and then formalises the road detection problem as an optimisation one.

关键词： electric vehicles image processing optimisation road vehicles tracking ant colony optimisation algorithm electrical vehicle nonstructured road detection nonstructured road tracking probability road detection problem tracking Optimization algorithms Land transportation road vehicles Road Electric vehicles Image processing ants ant algorithm

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Improving the efficiency of ant algorithms using adaptive refinement: Application to storm water Petwork design

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ADVANCES IN WATER RESOURCES 2006年第9期29卷 1371-1382页

作者： Afshar, M. H. Iran Univ Sci & Technol Dept Civil Engn Tehran Iran

The ant algorithm is a new evolutionary optimization method proposed for the solution of discrete combinatorial optimization problems. Many engineering optimization problems involve decision variables of continuous nature. Application of the ant algorithm to the optimization of these continuous problems requires discretization of the continuous search space, thereby reducing the underlying continuous problem to a discrete optimization problem. The level of discretization of the continuous search space, however, could present some problems. Generally, coarse discretization of the continuous design variables could adversely affect the quality of the final solution while finer discretization would enlarge the scale of the problem leading to higher computation cost and, occasionally, to low quality solutions. An adaptive refinement procedure is introduced in this paper as a remedy for the problem just outlined. The method is based on the idea of limiting the originally wide search space to a smaller one once a locally converged solution is obtained. The smaller search space is designed to contain the locally optimum solution at its center. The resulting search space is discretized and a completely new search is conducted to find a better solution. The procedure is continued until no improvement can be made by further refinement. The method is applied to a benchmark problem in storm water network design discipline and the results are compared with those of existing methods. The method is shown to be very effective and efficient regarding the optimality of the solution, and the convergence characteristics of the resulting ant algorithm. Furthermore, the method proves itself capable of finding an optimal, or near-optimal solution, independent of the discretization level and the size of the colony used. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： ant algorithm adaptive refinement optimal design storm water network

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