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Continuous space ant colony algorithm for automatic selection of orthophoto mosaic seamline network

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2022年 186卷 201-217页

作者： Wang, Qingyang Zhou, Guoqing Song, Ruhao Xie, Yongfan Luo, Mengyuan Yue, Tao Guilin Univ Technol Coll Earth Sci Guilin 541006 Peoples R China Guilin Univ Technol Guangxi Key Lab Spatial Informat & Geomat Guilin 541004 Peoples R China Guilin Univ Technol Coll Geomat & Geoinformat Guilin 541004 Peoples R China

This paper proposes a novel method for selecting orthophoto mosaic seamline network using a continuous space ant colony algorithm. The method first establishes a cost function based on gray differences and a gray gradient in the overlapping area. Then, a continuous space ant colony algorithm is established to iteratively select a least cost path for the optimal mosaic seamline. This proposed algorithm is carried out in three steps: (1) generate the initial mosaic network using area Voronoi diagrams with overlap;(2) adjust the vertices (The intersection of mosaic seamline network);and (3) search the seamlines using the continuous space ant colony algorithm to obtain the globally optimal mosaic seamline network. Two groups of experiments were performed using different numbers of orthophotos. The results demonstrate that our method saves 50 s and 53 s compared with the commercial software OrthoVista in Dataset 1 and Dataset 2, and saves 19 s and 22 s compared with Dijkstra in Dataset 1 and Dataset 2, respectively. The average cost of the mosaic seamline network generated by our method is the lowest (15.56) out of OrthoVista (21.79), Dijkstra (18.35) and Qmosaic (18.68) in Dataset 1, and the lowest (19.28) out of OrthoVista (26.33), Dijkstra (22.60) and Qmosaic (23.56) in Dataset 2.

关键词： Mosaic seamline Continuous space ant colony algorithm Automatic selection Orthophoto

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Solving minimum constraint removal (MCR) problem using a social-force-model-based ant colony algorithm

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APPLIED SOFT COMPUTING 2016年 43卷 553-560页

作者： Xu, Bo Min, Huaqing S China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China

The minimum constraint removal (MCR) motion planning problem aims to remove the minimum geometric constraints necessary for removing a free path that connects the starting point and the target point. In essence, discrete MCR problems are non-deterministic polynomial-time (NP)-hard problems;there is a "combinatorial explosion" phenomenon in solving such problems on a large scale. Therefore, we are searching for highly efficient approximate solutions. In the present study, an ant colony algorithm was used to solve these problems. The ant colony algorithm was improved based on the social force model during the solving process, such that it was no longer easy for the algorithm to fall into local extreme, and the algorithm was therefore suitable for solving the MCR problem. The results of the simulation experiments demonstrated that the algorithm used in the present study was superior to the exact algorithm and the greedy algorithm in terms of solution quality and running time. (C) 2016 Elsevier B.V. All rights reserved.

关键词： ant colony algorithm Discrete MCR Motion planning problem Robot path planning Social force model

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Optimal ant colony algorithm for UAV airborne LiDAR route planning in densely vegetated areas

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JOURNAL OF APPLIED REMOTE SENSING 2023年第4期17卷

作者： Tang, Feifei Li, Kunyang Xu, Feng Han, Ling Zhang, Huan Yang, Zhixing Chongqing Jiaotong Univ Sch Smart City Chongqing Peoples R China China Merchants Chongqing Commun Res & Design Inst Chongqing Peoples R China

In order to solve the problems of redundant data acquisition and sparse ground points in dense vegetation areas by conventional unmanned aerial vehicle (UAV) path planning methods, an UAV-airborne LiDAR route optimization method for dense vegetation areas is proposed. First, based on the high-resolution true color remote sensing images of the study area, the "fuzzy" calculation of vegetation coverage for route planning is completed. Then, an optimized ant colony algorithm is proposed for route planning, which introduces vegetation coverage as a reference for route planning and optimizes the pheromone initialization, state transfer rules, pheromone calculation, and update strategies in the classical ant colony algorithm to obtain more ground points. The experimental results show that this method can take into account the vegetation coverage of the flight area and find the area with low vegetation coverage to complete the route planning and efficiently use the sweeping principle of three-dimensional laser scanning to improve the probability of ground point acquisition, with faster iteration speed than the classical ant colony algorithm, and improve the efficiency of ground point acquisition in dense vegetation areas.

关键词： unmanned aerial vehicles path planning three-dimensional laser scanning vegetation coverage ant colony algorithm

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An Optimization Method for Satellite Data Structure Design Based on Improved ant colony algorithm

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IEEE ACCESS 2023年 11卷 64941-64956页

作者： Zhao, Jinchen Ye, Mian Xihua Univ Sch Aeronaut & Astronaut Chengdu 610039 Sichuan Peoples R China Minist Educ Intelligent Air Ground Fus Vehicles & Engn Res Ctr Chengdu 610039 Sichuan Peoples R China

The telemetry data structure is the embodiment of satellite telemetry format, the rationality and correctness of which determine the satellite telemetry capacity as well as transmission capability. Conventional satellite telemetry data structure designs excessively depend on manual experience, easily leading to problems such as waste of satellite resources, low telemetry transmission efficiency and unintuitive ground decoding. In this paper, a novel method was proposed to optimize the design of satellite telemetry data structure, based on rasterized modeling to visualize the design constraints, and an improved ant colony algorithm with grey relational analysis to optimize the telemetry data structure. The method is not only effective in preventing the deficiencies of conventional approaches, but also beneficial to the rational allocation of telemetry resources. The feasibility was verified by the telemetry data of a satellite with DFH-4 platform, and the results showed favorable convergence, along with valid improvement of the design process efficiency and application effect.

关键词： ant colony algorithm optimization design satellite data structure satellite telemetry

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Optimum buckling design of composite stiffened panels using ant colony algorithm

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COMPOSITE STRUCTURES 2010年第3期92卷 712-719页

作者： Wang, Wei Guo, S. Chang, Nan Yang, Wei Cranfield Univ Cranfield MK43 0AL Beds England NW Polytech Univ Coll Aeronaut Xian 710072 Peoples R China Chengdu Aircraft Design & Res Inst Chengdu 610041 Peoples R China

Optimal design of laminated composite stiffened panels of symmetric and balanced layup with different number of T-shape stiffeners is investigated and presented. The stiffened panels are simply supported and subjected to uniform biaxial compressive load. In the optimization for the maximum buckling load without weight penalty, the panel skin and the stiffened laminate stacking sequence, thickness and the height of the stiffeners are chosen as design variables. The optimization is carried out by applying an ant colony algorithm (ACA) with the ply contiguous constraint taken into account. The finite strip method is employed in the buckling analysis of the stiffened panels. The results shows that the buckling load increases dramatically with the number of stiffeners at first, and then has only a small improvement after the number of stiffeners reaches a certain value. An optimal layup of the skin and stiffener laminate has also been obtained by using the ACA. The methods presented in this paper should be applicable to the design of stiffened composite panels in similar loading conditions. Crown Copyright (C) 2009 Published by Elsevier Ltd. All rights reserved.

关键词： Composite stiffened panels Laminate stacking sequence Buckling ant colony algorithm

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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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Path planning of mobile robot based on adaptive ant colony algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5329-5338页

作者： Zheng, Yan Luo, Qiang Wang, Haibao Wang, Changhong Chen, Xin Chongqing Three Gorges Univ Sch Mech Engn Wanzhou 404100 Peoples R China Chongqing Engn Technol Res Ctr Light Alloy & Proc Chongqing Peoples R China Chongqing Univ Engn Res Ctr Intelligent Mfg Pilot Technol Chongqing Peoples R China

The traditional ant colony algorithm has some problems, such as low search efficiency, slow convergence speed and local optimum. To solve those problems, an adaptive heuristic function is proposed, heuristic information is updated by using the shortest actual distance, which ant passed. The reward and punishment rules are introduced to optimize the local pheromone updating strategy. The state transfer function is optimized by using pseudo-random state transition rules. By comparing with other algorithms' simulation results in different simulation environments, the results show that it has effectiveness and superiority on path planning.

关键词： Mobile robot path planning ant colony algorithm heuristic information global optimization

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Massive Music Resources Retrieval Method Based on ant colony algorithm

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2024年第5期18卷 1208-1222页

作者： Meng, Yun Commun Univ Shanxi Performance Sch Jinzhong 030619 Peoples R China

Music resources are characterized by quantization, diversification and complication. With the rapid increase of the demand for music resources, the storage of music resources is very large. In order to improve the retrieval effect of music resources, a massive music resources retrieval method based on ant colony algorithm is proposed to effectively use music resources. This paper constructs autocorrelation function to extract pitch feature of music resource, classifies the music resource information by calculating feature similarity. Using ant colony algorithm to correlate the feature of music resource, gain the result of correlative, locate the result of detection and get the result of multi-module. Simulation results show that the proposed method has high precision and recall, short retrieval time and can effectively retrieve massive music resources.

关键词： ant colony algorithm music resource autocorrelation function feature similarity information gain

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Ensemble classifier system based on ant colony algorithm and its application in chemical pattern classification

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2006年第1期80卷 39-49页

作者： He, YJ Chen, DZ Zhao, WX Zhejiang Univ Dept Chem Engn & Biol Engn Hangzhou 310027 Peoples R China Clarkson Univ Ctr Air Resources Engn & Sci Potsdam NY 13699 USA

A novel ant colony algorithm, mass recruitment and group recruitment based continuous ant colony optimization (MG-CACO), is proposed to solve continuous optimization problems. MG-CACO, which can capture the interdependencies between attributes and does not need discretization as a preprocessing step for optimization, was applied to extract classification rules from samples. To improve the predictive performance of the classifier, the ensemble strategy was adopted and the MG-CACO based ensemble classifier system called MG-CACO-ECS was built. Several datasets, obtained from UCI (University of California, Irvine) machine learning repository, were employed to illustrate the validity of MG-CACO-ECS. The results indicated that MG-CACO-ECS has satisfactory prediction accuracy. Furthermore, the problem of the producing area discrimination of olive oil was studied, and the obtained results demonstrated that MG-CACO-ECS has better prediction accuracy than the reported results. (c) 2005 Elsevier B.V. All rights reserved.

关键词： ant colony algorithm group recruitment mass recruitment rule extraction ensemble classifier system

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