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Adaptive elite ant colony optimization for track planning in gravity-aided navigation with multi-feature fusion 认领 引用
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作者 Jinbai Zhang Hao Zhou +4 位作者 Yun Xiao Xinshang Li Hong Li Yifeng Chen Zhicai Luo 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第5期112-126,共15页
Autonomous Underwater Vehicle track planning is critical for maritime defense missions,particularly in signal-denied and stealth-sensitive environments.Gravity-aided inertial navigation systems(GAINS),as a passive and... Autonomous Underwater Vehicle track planning is critical for maritime defense missions,particularly in signal-denied and stealth-sensitive environments.Gravity-aided inertial navigation systems(GAINS),as a passive and emission-free approach,offer strong potential for such missions.However,track planning under gravity constraints remains underexplored.This paper proposes an Adaptive Elite Ant Colony Optimization(AEACO)algorithm to address this gap.AEACO integrates two key strategies:an elite reinforcement mechanism inspired by genetic algorithms and a dynamic parameter adjustment method for pheromone-related variables.A gravity adaptability model is first established using fuzzy statistics and entropy-weighted feature fusion to identify navigable regions.AEACO then reinforces elite path segments and self-adjusts its parameters in response to gravity field variations.Experiments across 22real-world marine gravity scenarios show that AEACO consistently outperforms various classical methods.Specifically,it achieves up to 19%shorter paths,40%fewer turns,and 95%faster convergence.Unlike other Ant Colony Optimization(ACO)variants,AEACO operates without fixed parameters or external tuning,making it scalable and adaptable for real-time defense operations in complex underwater environments. 展开更多
关键词 Ant colony optimization Elite strategy Gravity-aided inertial navigation Track planning Gravity adaptive regions
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An anisotropy-dynamic ant colony optimization with probabilistic fracture uncertainty quantification for sub-seismic fault detection 认领 引用
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作者 Shi-Chang Li Yang Zhao +5 位作者 Cheng-Gang Xian Qi-Ya Qiao Fu-Yu Zhu Xing Liang Jie-Hui Zhang Lan-Lan Yan 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2527-2544,共18页
The precise characterization of subsurface fracture systems,especially sub-seismicfractures below seismic resolution,is critical for developing complex hydrocarbon reservoirs.While antcolony optimization(Aco)introduce... The precise characterization of subsurface fracture systems,especially sub-seismicfractures below seismic resolution,is critical for developing complex hydrocarbon reservoirs.While antcolony optimization(Aco)introduced"ant tracking"for seismicfracture detection,traditional methods rely on isotropic post-stack attributes,ignoring azimuthal anisotropy—a key indicator of fracture orientation and density.The azimuth-aware anisotropic bayes ACO(Ani-Bayes ACO)integrated pre-stack anisotropy via Bayesian priors but suffered from deterministic constraints and staticheuristics,limiting its ability to model conjugate fracture systems or parameter uncertainty.To resolve these limitations,we propose the anisotropy-dynamic ACO(ADACO)algorithm.ADACO replaces deterministic constraints with probabilistic,dynamically evolving fracture parameter distributions:von Mises for orientation and lognormal for density,both parameterized by elliptical fitting credibility.During optimization,a Hidden Markov Model(HMM)globally evaluates path consistency,while elite-path feedback iteratively focuses the distributions.Thisenablesuncertainty-quantified fracture prediction,multi-settracking,and autonomous adaptation to fracture clustering.Validation in a complex shale gas reservoir showed 85%consistency with drilling data-a significant improvement over Ani-Bayes AcO(46%).ADAcO thus provides a robust tool for sub-seismic fracture characterization. 展开更多
关键词 Ant colony optimization Fracture detection Azimuthal anisotropy Hidden Markov Model(HMM) Uncertainty quantification
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AI-driven triple-optimization based on ant colony optimization for minimizing deformation energy in three-dimensional analysis of bottom-hole assemblies 认领 引用
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作者 Qinfeng DI Dakun LUO +4 位作者 Heyuan YANG Tianxin LI Wenchang WANG Feng CHEN H.ZHANG 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2026年第6期1417-1432,共16页
An accurate analysis of the three-dimensional(3D)deformed configuration of a bottom-hole assembly(BHA)is critical for predicting and controlling well paths in directional drilling.Among the various numerical approache... An accurate analysis of the three-dimensional(3D)deformed configuration of a bottom-hole assembly(BHA)is critical for predicting and controlling well paths in directional drilling.Among the various numerical approaches,the weighted residual method exhibits superior performance owing to its semi-analytical nature,enabling high accuracy in handling diverse boundary conditions.In previous studies,the weighted residual method has been coupled with a dual optimization process to determine the 3D deformation and tangency point of the BHA.However,its applicability is limited by the conventional treatment of contact interactions between the BHA and wellbore wall.Specifically,stabilizer-wellbore contacts are regarded as predefined boundary conditions,rather than solving these contact positions as unknown variables consistent with actual downhole conditions.This limitation reduces modeling fidelity in complex downhole environments.To address this limitation,this study enhances the optimization-based weighted residual method by introducing ant colony optimization(ACO)to solve the 3D contact problem.In the proposed framework,the bending energy of the deformed BHA is conceptualized as“food”,while the contact positions and orientations of stabilizers are assigned a measure of“taste”.Through this metaphor,the ACO algorithm employs artificial“ants”to explore the optimal stabilizer locations and orientations that minimize the BHA bending energy,thereby refining the computed 3D deformation.The simulation results demonstrate that integrating ACO into the previously established dual optimization framework enables the effective determination of the contact configuration between the BHA and wellbore wall.As a result,the overall accuracy of the 3D BHA deformation analysis is significantly improved.In one representative case study,the bending energy of the BHA is reduced by 55.6%compared with that obtained from the original dual-optimization method. 展开更多
关键词 numerical approachesthe directional drillingamong weighted residual method dual optimization process Deformation Energy Bottom Hole Assembly predicting controlling well paths Ant Colony Optimization
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Bayesian-based ant colony optimization algorithm for edge detection 认领 引用
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作者 YU Yongbin ZHONG Yuanjingyang +6 位作者 FENG Xiao WANG Xiangxiang FAVOUR Ekong ZHOU Chen CHENG Man WANG Hao WANG Jingya 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第4期892-902,共11页
Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of t... Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task. 展开更多
关键词 ant colony optimization(ACO) Bayesian algorithm edge detection transfer function.
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Max-Min Adaptive Ant Colony Optimization Approach to Multi-UAVs Coordinated Trajectory Replanning in Dynamic and Uncertain Environments 认领 引用 被引量：40
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作者 Hai-bin Duan Xiang-yin Zhang +1 位作者 Jiang Wu Guan-jun Ma 《Journal of Bionic Engineering》 SCIE EI 2009年第2期161-173,共13页
Multiple Uninhabited Aerial Vehicles(multi-UAVs)coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control.Based on the construction of the basic model o... Multiple Uninhabited Aerial Vehicles(multi-UAVs)coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control.Based on the construction of the basic model of multi-UAVs coordinated trajectory replanning,which includes problem description,threat modeling,constraint conditions,coordinated function and coordination mechanism,a novel Max-Min adaptive Ant Colony Optimization(ACO)approach is presented in detail.In view of the characteristics of multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments,the minimum and maximum pheromone trails in ACO are set to enhance the searching capability,and the point pheromone is adopted to achieve the collision avoidance between UAVs at the trajectory planner layer.Considering the simultaneous arrival and the air-space collision avoidance,an Estimated Time of Arrival(ETA)is decided first.Then the trajectory and flight velocity of each UAV are determined.Simulation experiments are performed under the complicated combating environment containing some static threats and popup threats.The results demonstrate the feasibility and the effectiveness of the proposed approach. 展开更多
关键词 Multiple Uninhabited Aerial Vehicles(multi-UAVs) Ant Colony Optimization(ACO) trajectory replanning collision avoidance Estimated Time of Arrival(ETA)
A Bayesian Network Learning Algorithm Based on Independence Test and Ant Colony Optimization 认领 引用 被引量：21
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作者 JI Jun-Zhong ZHANG Hong-Xun +1 位作者 HU Ren-Bing LIU Chun-Nian 《自动化学报》 EI CAS 北大核心 2009年第3期281-288,共8页
To solve the drawbacks of the ant colony optimization for learning Bayesian networks(ACO-B),this paper proposes an improved algorithm based on the conditional independence test and ant colony optimization(I-ACO-B).Fir... To solve the drawbacks of the ant colony optimization for learning Bayesian networks(ACO-B),this paper proposes an improved algorithm based on the conditional independence test and ant colony optimization(I-ACO-B).First,the I-ACO-B uses order-0 independence tests to effectively restrict the space of candidate solutions,so that many unnecessary searches of ants can be avoided.And then,by combining the global score increase of a solution and local mutual information between nodes,a new heuristic function with better heuristic ability is given to induct the process of stochastic searches.The experimental results on the benchmark data sets show that the new algorithm is effective and efficient in large scale databases,and greatly enhances convergence speed compared to the original algorithm. 展开更多
关键词 Uncertainty modeling Bayesian network structure learning ant colony optimization(ACO) conditional independencetest
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Improved ant colony optimization algorithm for the traveling salesman problems 认领 引用 被引量：26
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作者 Rongwei Gan Qingshun Guo +1 位作者 Huiyou Chang Yang Yi 《Journal of Systems Engineering and Electronics》 SCIE EI 2010年第2期329-333,共5页
Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is amo... Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness. 展开更多
关键词 ant colony optimization heuristic algorithm scout ants path evaluation model traveling salesman problem.
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Improved ant colony optimization for multi-depot heterogeneous vehicle routing problem with soft time windows 认领 引用 被引量：14
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作者 汤雅连 蔡延光 杨期江 《Journal of Southeast University(English Edition)》 EI CAS 2015年第1期94-99,共6页
Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a ... Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful. 展开更多
关键词 vehicle routing problem soft time window improved ant colony optimization customer service priority genetic algorithm
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Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm 认领 引用 被引量：12
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作者 Duan Hai-bin Wang Dao-bo Yu Xiu-fen 《Journal of Bionic Engineering》 EI 2006年第2期73-78,共6页
This paper presents an application of an Ant Colony Optimization （ACO） algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorith... This paper presents an application of an Ant Colony Optimization （ACO） algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm, an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response. 展开更多
关键词 Ant Colony Optimization algorithm pheromone nonlinear PID parameter optimization
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A Graph-based Ant Colony Optimization Approach for Integrated Process Planning and Scheduling 认领 引用 被引量：5
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作者 王进峰 范孝良 +1 位作者 张超炜 万书亭 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2014年第7期748-753,共6页
This paper considers an ant colony optimization algorithm based on AND/OR graph for integrated process planning and scheduling(IPPS). Generally, the process planning and scheduling are studied separately. Due to the c... This paper considers an ant colony optimization algorithm based on AND/OR graph for integrated process planning and scheduling(IPPS). Generally, the process planning and scheduling are studied separately. Due to the complexity of manufacturing system, IPPS combining both process planning and scheduling can depict the real situation of a manufacturing system. The IPPS is represented on AND/OR graph consisting of nodes, and undirected and directed arcs. The nodes denote operations of jobs, and undirected/directed arcs denote possible visiting path among the nodes. Ant colony goes through the necessary nodes on the graph from the starting node to the end node to obtain the optimal solution with the objective of minimizing makespan. In order to avoid local convergence and low convergence, some improved strategy is incorporated in the standard ant colony optimization algorithm. Extensive computational experiments are carried out to study the influence of various parameters on the system performance. 展开更多
关键词 Process planning Scheduling Ant colony optimization Makespan
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Ant Colony Optimization for Task Allocation in Multi-Agent Systems 认领 引用 被引量：5
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作者 王鲁 王志良 +1 位作者 胡四泉 刘磊 《China Communications》 SCIE CSCD 2013年第3期125-132,共8页
Task allocation is a key issue of agent cooperation mechanism in Multi-Agent Systems. The important features of an agent system such as the latency of the network infrastructure, dynamic topology, and node heterogenei... Task allocation is a key issue of agent cooperation mechanism in Multi-Agent Systems. The important features of an agent system such as the latency of the network infrastructure, dynamic topology, and node heterogeneity impose new challenges on the task allocation in Multi-Agent environments. Based on the traditional parallel computing task allocation method and Ant Colony Optimization (ACO), a novel task allocation method named Collection Path Ant Colony Optimization (CPACO) is proposed to achieve global optimization and reduce processing time. The existing problems of ACO are analyzed; CPACO overcomes such problems by modifying the heuristic function and the update strategy in the Ant-Cycle Model and establishing a threedimensional path pheromone storage space. The experimental results show that CPACO consumed only 10.3% of the time taken by the Global Search Algorithm and exhibited better performance than the Forward Optimal Heuristic Algorithm. 展开更多
关键词 multi-agent systems task alloca- tion ant colony optimization efficiency factor
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An Ant Colony Optimization Based Dimension Reduction Method for High-Dimensional Datasets 认领 引用 被引量：4
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作者 Ying Li Gang Wang +2 位作者 Huiling Chen Lian Shi Lei Qin 《Journal of Bionic Engineering》 SCIE EI CSCD 2013年第2期231-241,共11页
In this paper, a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection （ACO-S） is proposed for high-dimensional datasets. Because microarray datasets comprise tens o... In this paper, a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection （ACO-S） is proposed for high-dimensional datasets. Because microarray datasets comprise tens of thousands of features （genes）, they are usually used to test the dimension reduction techniques. ACO-S consists of two stages in which two well-known ACO algorithms, namely ant system and ant colony system, are utilized to seek for genes, respectively. In the first stage, a modified ant system is used to filter the nonsignificant genes from high-dimensional space, and a number of promising genes are reserved in the next step. In the second stage, an improved ant colony system is applied to gene selection. In order to enhance the search ability of ACOs, we propose a method for calculating priori available heuristic information and design a fuzzy logic controller to dynamically adjust the number of ants in ant colony system. Furthermore, we devise another fuzzy logic controller to tune the parameter （q0） in ant colony system. We evaluate the performance of ACO-S on five microarray datasets, which have dimensions varying from 7129 to 12000. We also compare the performance of ACO-S with the results obtained from four existing well-known bionic optimization algorithms. The comparison results show that ACO-S has a notable ability to＂ generate a gene subset with the smallest size and salient features while yielding high classification accuracy. The comparative results generated by ACO-S adopting different classifiers are also given. The proposed method is shown to be a promising and effective tool for mining high-dimension data and mobile robot navigation. 展开更多
关键词 gene selection feature selection ant colony optimization high-dimensional data
Underwater Target Detection Based on Reinforcement Learning and Ant Colony Optimization 认领 引用 被引量：6
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作者 WANG Xinhua ZHU Yungang +1 位作者 LI Dayu ZHANG Guang 《Journal of Ocean University of China》 SCIE CAS CSCD 2022年第2期323-330,共8页
Underwater optical imaging produces images with high resolution and abundant information and hence has outstanding advantages in short-distance underwater target detection.However,low-light and high-noise scenarios po... Underwater optical imaging produces images with high resolution and abundant information and hence has outstanding advantages in short-distance underwater target detection.However,low-light and high-noise scenarios pose great challenges in un-derwater image and video analyses.To improve the accuracy and anti-noise performance of underwater target image edge detection,an underwater target edge detection method based on ant colony optimization and reinforcement learning is proposed in this paper.First,the reinforcement learning concept is integrated into artificial ants’movements,and a variable radius sensing strategy is pro-posed to calculate the transition probability of each pixel.These methods aim to avoid undetection and misdetection of some pixels in image edges.Second,a double-population ant colony strategy is proposed,where the search process takes into account global search and local search abilities.Experimental results show that the algorithm can effectively extract the contour information of underwater targets and keep the image texture well and also has ideal anti-interference performance. 展开更多
关键词 ant colony optimization reinforcement learning underwater target edge detection
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Hybrid ant colony optimization for the resource-constrained project scheduling problem 认领 引用 被引量：10
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作者 Linyi Deng Yan Lin Ming Chen 《Journal of Systems Engineering and Electronics》 SCIE EI 2010年第1期67-71,共5页
To solve the resource-constrained project scheduling problem(RCPSP),a hybrid ant colony optimization(HACO)approach is presented.To improve the quality of the schedules,the HACO is incorporated with an extended double ... To solve the resource-constrained project scheduling problem(RCPSP),a hybrid ant colony optimization(HACO)approach is presented.To improve the quality of the schedules,the HACO is incorporated with an extended double justification in which the activity splitting is applied to predict whether the schedule could be improved.The HACO is tested on the set of large benchmark problems from the project scheduling problem library(PSPLIB).The computational result shows that the proposed algo-rithm can improve the quality of the schedules efficiently. 展开更多
关键词 project scheduling double justification ant colony optimization activity splitting.
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A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem 认领 引用 被引量：2
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作者 Chuan Wang Ruoyu Zhu +4 位作者 Yi Jiang Weili Liu Sang-Woon Jeon Lin Sun Hua Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第5期1209-1228,共20页
The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant... The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively. 展开更多
关键词 Dynamic traveling salesman problem(DTSP) offline optimization and online application ant colony optimization(ACO) two-optimization(2-opt)strategy
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Automatic teaching of stereovision-guided welding robot using ant colony optimization algorithm 认领 引用 被引量：3
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作者 李鹤喜 石永华 王国荣 《China Welding》 EI CAS 2010年第1期37-42,共6页
A binocular stereovision system with a linear laser emitter is developed to detect seam position and its orientation, employing acquired 3-dimensional seam data, the automatic teaching of welding robot is implemented ... A binocular stereovision system with a linear laser emitter is developed to detect seam position and its orientation, employing acquired 3-dimensional seam data, the automatic teaching of welding robot is implemented using a controlling strategy based on ant colony optimization（ ACO ） algorithm, in which the angle increment of robot joint is discretized as the nodes of ACO graph and a corresponding pheromone updating strategy is presented. The experimental results for curvilinear seams and saddle-shaped seams show that the automatic teaching of welding robot can be successfully completed using the ACO-based controlling strategy under the guidance of stereovision, and the welding trajectory generated by the proposed method has higher accuracy and less setting time compared with conventional proportional-integral-differential （PID） controller and fuzzy controller. 展开更多
关键词 stereovision automatic teaching robot ant colony optimization
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Bayesian network learning algorithm based on unconstrained optimization and ant colony optimization 认领 引用 被引量：3
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作者 Chunfeng Wang Sanyang Liu Mingmin Zhu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第5期784-790,共7页
Structure learning of Bayesian networks is a wellresearched but computationally hard task.For learning Bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony opt... Structure learning of Bayesian networks is a wellresearched but computationally hard task.For learning Bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization（U-ACO-B） to solve the drawbacks of the ant colony optimization（ACO-B）.In this algorithm,firstly,an unconstrained optimization problem is solved to obtain an undirected skeleton,and then the ACO algorithm is used to orientate the edges,thus returning the final structure.In the experimental part of the paper,we compare the performance of the proposed algorithm with ACO-B algorithm.The experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm. 展开更多
关键词 Bayesian network structure learning ant colony optimization unconstrained optimization
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An effective multi-level algorithm based on ant colony optimization for graph bipartitioning 认领 引用 被引量：3
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作者 冷明 郁松年 +1 位作者 丁旺 郭强 《Journal of Shanghai University(English Edition)》 2008年第5期426-432,共7页
Partitioning is a fundamental problem with applications to many areas including data mining, parellel processing and Very-large-scale integration （VLSI） design. An effective multi-level algorithm for bisecting graph... Partitioning is a fundamental problem with applications to many areas including data mining, parellel processing and Very-large-scale integration （VLSI） design. An effective multi-level algorithm for bisecting graph is proposed. During its coarsening phase, an improved matching approach based on the global information of the graph core is developed with its guidance function. During the refinement phase, the vertex gain is exploited as ant＇s heuristic information and a positive feedback method based on pheromone trails is used to find the global approximate bipartitioning. It is implemented with American National Standards Institute （ANSI） C and compared to MeTiS. The experimental evaluation shows that it performs well and produces encouraging solutions on 18 different graphs benchmarks. 展开更多
关键词 rain-cut graph bipartitioning multi-level algorithm ant colony optimization （ACO）
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Global path planning approach based on ant colony optimization algorithm 认领 引用 被引量：12
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作者 文志强 蔡自兴 《Journal of Central South University of Technology》 2006年第6期707-712,共6页
Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, concepti... Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the goal. Then the whole path was divided into three parts and ACO was used to search the second part path. When the three parts pathes were adjusted, the final path was found. The valid path and invalid path were defined to ensure the path valid. Finally, the strategies of the pheromone search were applied to search the optimum path. However, when only the pheromone was used to search the optimum path, ACO converges easily. In order to avoid this premature convergence, combining pheromone search and random search, a hybrid ant colony algorithm(HACO) was used to find the optimum path. The comparison between ACO and HACO shows that HACO can be used to find the shortest path. 展开更多
关键词 mobile robot ant colony optimization global path planning pheromone
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Cooperative Search of UAV Swarm Based on Ant Colony Optimization with Artificial Potential Field 认领 引用 被引量：4
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作者 XING Dongjing ZHEN Ziyang +1 位作者 ZHOU Chengyu GONG Huajun 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2019年第6期912-918,共7页
An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed... An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed architecture where each UAV is considered as an ant and makes decision autonomously.At each decision step,the ants choose the next gird according to the state transition rule and update its own artificial potential field and pheromone map based on the current search results.Through iterations of this process,the cooperative search of UAV swarm for mission area is realized.The state transition rule is divided into two types.If the artificial potential force is larger than a threshold,the deterministic transition rule is adopted,otherwise a heuristic transition rule is used.The deterministic transition rule can ensure UAVs to avoid the threat or approach the target quickly.And the heuristics transition rule considering the pheromone and heuristic information ensures the continuous search of area with the goal of covering more unknown area and finding more targets.Finally,simulations are carried out to verify the effectiveness of the proposed ACOAPF algorithm for cooperative search mission of UAV swarm. 展开更多
关键词 ant colony optimization artificial potential field cooperative search unmanned aerial vehicle(UAV)swarm
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