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33rd Chinese Control and Decision Conference (CCDC)

作者： Sun, Jiuxiang Yu, Ya'nan Xin, Ling Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266042 Peoples R China

ISBN: (纸本)9781665440899

Path planning is a key problem in the motion control of mobile robot. In order to solve the problem that the traditional storage mode of automatic container terminal affects the overall operation efficiency, this paper puts forward a matrix yard storage mode, which is transformed into grid map model, and then uses ant colony optimization algorithm to plan the path of AGV. Aiming at the shortcomings of traditional ant colony optimization algorithm (ACO) in global path planning, such as slow convergence speed and weak optimization ability, an improved ant colony path planning algorithm is proposed. Firstly, the grid map is established, and the fruit fly optimization algorithm (FOA) is used for fast pre-search on the grid map to generate the original pheromone distribution required by the ant colony optimization algorithm, and then the ant colony optimization algorithm is used for global path planning. At the same time, in order to solve the problem of many path turning angles and large cumulative turning angles in the planning, the path smoothing is carried out. The simulation results show that the improved algorithm has fewer turns and smoother path, and the improved ant colony algorithm has a greater improvement in path search speed and accuracy than the traditional algorithm.

关键词： ant colony optimization algorithm Fruit fly optimization algorithm matrix yard storage mode Smoothing

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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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An ant colony optimization algorithm for the Min-Degree Constrained Minimum Spanning Tree Problem

An Ant Colony Optimization Algorithm for the Min-Degree Cons...

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4th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Murthy, V. Venkata Ramana Singh, Alok Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

ISBN: (纸本)9783319037554;9783319037561

Given a connected edge-weighted undirected graph, the min-degree constrained minimum spanning tree (MDCMST) problem seeks on this graph a spanning tree of least cost in which every non-leaf node have a degree of at least d in the spanning tree. This problem is NP-Hard for 3 <= d <= left perpendicularn/2right perpendicular where n is the number of nodes in the graph. In this paper, we have proposed an ant colony optimization based approach to this problem. The proposed approach has been tested on Euclidean and random instances both. Computational results show the effectiveness of the proposed approach.

关键词： ant colony optimization algorithm Combinatorial optimization Min-Degree Constrained Minimum Spanning Tree Problem Swarm Intelligence

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Integrated Design for Assembly Approach Using ant colony optimization algorithm for Optimal Assembly Sequence Planning 4th

Integrated Design for Assembly Approach Using Ant Colony Opt...

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4th International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Murali, G. Bala Deepak, B. B. V. L. Biswal, B. B. Khamari, Bijaya Kumar Natl Inst Technol Dept Ind Design Rourkela 769008 Odisha India

ISBN: (纸本)9789811080555;9789811080548

To reduce the assembly efforts and cost of the assembly, researchers are motivated to reduce the part number by applying design for assembly (DFA) concept. The so far existed literature review has no generalized method to obtain optimum assembly sequence by incorporating the DFA concept. Even though the DFA concept is applied separately, still it demands high-skilled user intervention to obtain optimum assembly sequence. As the assembly sequence planning (ASP) is NP-hard and multi-objective optimization problem, it requires more computational time and huge search space. In this paper, an attempt is made to combine DFA concept along with ASP problem to obtain optimum assembly sequence. ant colony optimization algorithm (ACO) is used for combining DFA and ASP problem by considering directional changes as fitness function to obtain optimum feasible assembly sequences. Generally, the product with 'N' parts consists of N - 1 levels during assembly, which are reduced by applying DFA concept. Later on, optimum assembly sequence can be obtained for the reduced levels of assembly using different assembly predicates.

关键词： Design for assembly Assembly sequence planning ant colony optimization algorithm Multi-objective optimization

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Adaptive Plot System for Serious Emerging Games based on the ant colony optimization algorithm 45

Adaptive Plot System for Serious Emerging Games based on the...

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45th Latin American Computing Conference (CLEI)

作者： Aguilar, Jose Altamiranda, Junior Diaz, Francisco Gutierrez de Mesa, Jose Pinto, Angel Univ Andes Fac Ingn CEMISID Merida Venezuela Univ Alcala Dept Ciencias Comp Madrid Spain Univ Sinu Dept Ingn Sistemas Monteria Colombia

ISBN: (纸本)9781728155746

A serious emerging games engine (SEGE) must make explicit the possibility of emergence in a serious game (SG), from the coordinated handling of game plot, adapted to the specific educational context where it is being developed. In previous articles a SEGE based on the ant colony optimization algorithm (ACO) has been proposed, which allows the initial emergence of a SG. In the present work the adaptive plot system (APS) is specified, which allows the emergence of plot in a serious game emerging (SGE), and is based on an ACO that changes the plot in the game, according to the theme that is being given in a smart classroom SaCI (Salon de Clase Inteligente, for its acronym in Spanish),). The APS performs the management of a set of game plot that may be of interest in a context-educational domain, in such a way to adapt the SGE initially conceived to the subject taught in the SaCI, in order to make the appropriate SGE emerge Pedagogical process in progress. Additionally, this paper analyzes the behavior of APS in a case study, showing very encouraging results as SEGE.

关键词： Serious Emerging Games Engine Serious Emerging Games ant colony optimization algorithm Smart Classroom Videogame Adaptation System

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An ant colony optimization algorithm Based Image Classification Method for Content-Based Image Retrieval in Cloud Computing Environment

An Ant Colony Optimization Algorithm Based Image Classificat...

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Int Conf on Signal Processing, Image Processing and Pattern Recognition/Int Conf on Web Science and Engineer/Int Conf on Human Computer Interaction

作者： Seo, Kwang-Kyu Sangmyung Univ Dept Engn Management Cheonan 330720 Chungnam South Korea

ISBN: (纸本)9783642352690

Feature selection and feature extraction are the most important steps in classification problems. Feature selection is commonly used to reduce dimensionality of datasets with tens or hundreds of thousands of features which would be impossible to process further. One of the problems in which feature selection is essential is content-based image classification problems. This paper presents a novel method for image classification method based on an ant colony optimization algorithm which can significantly improve the classification performance for content-based image retrieval in cloud computing environment. ant colony optimization algorithm is inspired by observation on real ants in their search for the shortest paths to food sources. The proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. We compared the previous algorithm with the proposed algorithm in terms of image classification performance. As a result, the proposed algorithm showed higher performance in terms of accuracy.

关键词： Feature selection ant colony optimization algorithm Image classification Content-based image retrieval Cloud Computing

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The analysis of the convergence of ant colony optimization algorithm

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Frontiers of Electrical and Electronic Engineering in China 2007年第3期2卷 268-272页

作者： ZHU Qingbao WANG Lingling Department of Computer Science Nanjing Normal UniversityNanjing 210097China

The ant colony optimization algorithm has been widely studied and many important results have been *** this algorithm has been applied to many fields,the analysis about its convergence is much less,which will influence the improvement of this ***,the convergence of this algorithm applied to the traveling salesman problem(TSP)was analyzed in *** conclusion that this algorithm will definitely converge to the optimal solution under the condition of 0

关键词： ant colony optimization algorithm convergence analysis heuristic function TSP

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A Transmission Line Planning Method Based on Variable Size Grid and Improved ant colony optimization algorithm 38

A Transmission Line Planning Method Based on Variable Size G...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Liu, Zhuo Nan, Guojun Cheng, Yingying Du, Haibo Zhu, Wenwu Hefei University of Technology School of Electrical Engineering and Automation Hefei China

ISBN: (纸本)9798350303636

Route planning for transmission lines has always been an important part of the power construction industry. In the past, however, such planning has involved many critical steps, not only collecting complex geographic information data, but also performing personnel calculations. The collected data needed to be analyzed by engineers, and then safety and reliability were taken into account to determine the lowest economic cost route option. This traditional approach is not only very consuming for personnel, but also lacks reliability. This is because the development of the scheme is too dependent on the experience and decision making of the personnel. Now this paper proposes an intelligent route selection algorithm that improves on the ant colony optimization (ACO) algorithm. Unlike previous route planning algorithms that are based on fixed size grid (FSG) maps, this paper uses variable size grid (VSG) maps to replace the maps with fixed-size grids. This not only reduces the number of grids in the route search to speed up the search speed, but also better fits the terrain to improve the rationality of the route. Then the grid node structure of the variable size grid is optimized. Then the heuristic function of the ACO algorithm is improved, and the parallel search based on variable-size grids is adopted. This not only improves the efficiency, but also increases the chance of searching the global optimal route. Finally, route optimization is performed on the routes to achieve smoothing process and reduce the cost of the towers. © 2023 IEEE.

关键词： ant colony optimization algorithm parallel search smoothing process variable size grid

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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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Parallel ant colony optimization algorithm

Parallel ant colony optimization algorithm

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6th World Congress on Intelligent Control and Automation

作者： Liu, Hong Li, Ping Wen, Yu Zhejiang Univ Natl Lab Ind Control Technol Res Ctr Intelligent Transportat Syst Hangzhou 310027 Zhejiang Provin Peoples R China

ISBN: (纸本)1424403316

ant colony optimization algorithm is a good way to solve complex multi-stage decision problems. But the construction graph and computation steps for an ant to construct a solution in the construction graph will be exponentially increased, if the stage number and decision variable dimension of complex multi-stage decision problem are increasing. It will cause the ant colony optimization algorithm can not be computed by a single PC. Therefore, a parallel ant colony optimization algorithm based on the construction graph decomposition is presented to solve this problem. The parallel ant colony optimization algorithm decomposes the construction graph into some parts and each part is placed on different PC. The whole computation task is accomplished by mutual cooperation in the PCs which join in the computation. Experiment has verified that it can solve this problem and improve the computation efficiency.

关键词： parallel computing ant colony optimization algorithm multi-stage decision construction graph decomposition

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