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A modified ACO algorithm for virtual network embedding based on graph decomposition

COMPUTER COMMUNICATIONS 2016年第Apr.15期80卷 1-15页

作者： Zhu, Fangjin Wang, Hua Shandong Univ Sch Comp Sci & Technol Jinan Peoples R China

Network virtualization is a promising scheme to solve Internet ossification. A challenging problem in this scheme is virtual network embedding (VNE), which involves efficiently embedding multiple heterogeneous virtual networks into one or more physical networks. The VNE problem is known to be NP-hard and thus requires an approximate algorithm as a solution. This study models the VNE problem based on virtual network topology invariance and analyzes the shortcomings of a general embedding algorithm under different network topologies. A modified ant colony optimization algorithm is proposed based on network topology decomposition. A pre-computation algorithm is first proposed based on ant random walking to accelerate the recognition of the ring characteristics of a network topology. Pre-computation results are used to guide the decomposition of virtual networks and the embedding process of ring structures. The topology of a virtual network is decomposed into a combination of ring structures and tree structures, which have different characteristics. Different embedding algorithms are then designed for these structures. Point-disjoint paths are searched for any two virtual links to ensure the reliability of the network topology in the embedding process. The proposed algorithm shows an enhanced optimization performance, which is better than those of the ViNE-LB and GN-SP algorithms. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Network virtualization Virtual network embedding ant colony optimization algorithm Virtual network topology invariance Topology decomposition

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Improved ant colony algorithm for path planning of mobile robots based on compound prediction mechanism

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期44卷 2147-2162页

作者： Liu, Zhongpu Liu, Jianjuan Henan Univ Technol Sch Elect Engn Inst Mech & Elect Equipment & Measurement & Contr Zhengzhou Peoples R China

For the issues of the ant colony algorithm (ACO) to solving the problems in mobile robot path planning, such as the slowoptimization speed and the redundant paths in planning results, a high-precision improved ant colony algorithm (IPACO) with fast optimization and compound prediction mechanism is proposed. Firstly, aiming at maximizing the possibility of optimal node selection in the process of path planning, a composite optimal node prediction model is introduced to improve the state transition function. Secondly, a pheromone model with initialize the distribution and "reward or punishment" update mechanism is used to updates the global pheromone concentration directionally, which increases the pheromone concentration of excellent path nodes and the heuristic effect;Finally, a prediction-backward mechanism to deal with the "deadlock" problem in the ant colony search process is adopted in the IPACO algorithm, which enhance the success rate in the ACO algorithm path planning. Five groups of different environments are selected to compare and verify the performance of IPACO algorithm, ACO algorithm and three typical path planning algorithms. The experimental simulation results show that, compared with the ACO algorithm, the convergence speed and the planning path accuracy of the IPACO algorithm are improved by 57.69% and 12.86% respectively, and the convergence speed and the planning path accuracy are significantly improved;the optimal path length, optimization speed and stability of the IPACO algorithm are improved. Which verifies that the IPACO algorithm can effectively improve the environmental compatibility and stability of the ant colony algorithm path planning, and the effect is significantly improved.

关键词： Mobile robot Path planning Path prediction model ant colony optimization algorithm Reward and punishment update

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Multi-treatment capacitated arc routing of construction machinery in Taiwan's smooth road project

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AUTOMATION IN CONSTRUCTION 2012年第1期21卷 210-218页

作者： Huang, Shan-Huen Lin, Pei-Chun Natl Cheng Kung Univ Dept Transportat & Commun Management Sci Tainan 701 Taiwan Natl Kaohsiung First Univ Sci & Technol Dept Logist Management Kaohsiung 811 Taiwan

This study investigates the routing problems of road resurfacing in Taiwan's smooth road project, incorporating multiple treatments served by different construction machinery in a way that propagates extra time window constraints to ensure that a subsequent treatment starts after the required preceding treatment has started or been completed. The routing problem is modeled as a multi-treatment capacitated arc routing problem with time windows (MTCARPTW) in order to determine a set of trips at a minimum total cost that covers all required links of road resurfacing work. The MTCARPTW is first transformed into a traveling salesman problem (TSP);then, a heuristics method based on ant colony optimization (ACO) is applied and evaluated based on the set of given circumstances. The computational results indicate that the proposed algorithm is efficient. This research contributes to identifying a new routing problem, modeling this MTCARPTW, and introducing the ACO to solve the problem efficiently. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Smooth road project Arc routing problems ant colony optimization algorithm

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ant colony Based on Cat Swarm optimization and Application in Picking Robot Path Planning

Ant Colony Based on Cat Swarm Optimization and Application i...

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2016 IEEE 7th International Conference on Software Engineering and Service Science

作者： Yanqin Xun Mingshan Liu Yuan Zhou Rui Wang Wenbo Zhang College of Communication Engineering Jilin University

In view of solving multi-objective path planning in the static environment,there are some faults for ant colony optimization(ACO),such as the long computation and easy to fall into local *** solve these problems,the ACO based on cat swarm optimization(CSO) algorithm searching model(CSOACO) is *** this algorithm,the introduction of CSO algorithm search pattern realizes the local search in the current solution for ant colony individuals,which not only enrich the diversity of solution,but improve the accuracy of the ***,the new algorithm is simulated in MATLAB for picking robot multi-objective path planning *** the simulation analysis,not only set the parameters,but compare CSOACO with other *** results show that the algorithm can accelerate the convergence speed,search to the global optimal solution and realize the multiobjective path planning of picking robot.

关键词： ant colony optimization algorithm cat swarm optimization algorithm picking robot path planning

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Parameters identification and trajectory control for a hydraulic system

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ISA TRANSACTIONS 2019年 92卷 228-240页

作者： Feng, Hao Yin, Chenbo Ma, Wei Yu, Hongfu Cao, Donghui Nanjing Tech Univ United Inst Excavator Key Technol Nanjing 211816 Jiangsu Peoples R China Nanjing Tech Univ Inst Automobile & Construct Machinery Nanjing 211816 Jiangsu Peoples R China SANY Grp Co Ltd Suzhou 215300 Peoples R China

In order to improve the tracking accuracy of a hydraulic system, an improved ant colony optimization algorithm (IACO) is proposed to optimize the values of proportional-integral-derivative (PID) controller. In addition, this paper presents an experimental study on the parameters identification to deduce accurate numerical values of the hydraulic system, which also determines the relationship between control signal and output displacement. Firstly, the basic principle of the hydraulic system and the mathematical model of the electro-hydraulic proportional control system are analyzed. Based on the theoretical models, the transfer function of the control system is obtained by recursive least square identification method (RLS). Then, the key parameters of the control system model are obtained. Some improvements are proposed to avoid premature convergence and slow convergence rate of ACO: the transition probability is revised based adjacent search mechanism, dynamic pheromone evaporation coefficient adjustment strategy is adopted, pheromone update rule and parameters optimization range are also improved. Then the proposed IACO tuning based PID controller and the identification parameters are modeled and simulated using MATLAB/Simulink and AMESim co-simulation platform. Comparisons of IACO, standard ACO and Ziegler-Nichols (Z-N) PID controllers are carried out with different references as step signal and sinusoidal wave using the co-simulation platform. The simulation results of the bucket system using the proposed controller demonstrates improved settling time, rise time and the convergence speed with a new objective function J. Finally, experiments with leveling operations are performed on a 23 ton robotic excavator. The experimental results show that the proposed controller improves the trajectory accuracy of the leveling operation by 28% in comparison to the standard ACO-PID controller. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Parameters identification Trajectory control ant colony optimization algorithm PID Recursive least square method Robotic excavator

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A framework for modeling fault propagation paths in air turbine starter based on Bayesian network

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2022年第6期236卷 1078-1095页

作者： Guo, Runxia Wang, Zihang Civil Aviat Univ China Sch Elect Informat & Automat Dongli Dist Tianjin Jin North Rd 2898 Tianjin 300300 Peoples R China

Any minor fault may spread, accumulate and enlarge through the causal link of fault in a closed-loop complex system of civil aircraft, and eventually result in unplanned downtime. In this paper, the fault propagation path model (FPPM) is proposed for system-level decomposition and simplifying the process of fault propagation analysis by combining the improved ant colony optimization algorithm (I-ACO) with the Bayesian network (BN). In FPPM, the modeling of the fault propagation path consists of three models, namely shrinking model (SM), ant colony optimization model (ACOM), and assessment model (AM). Firstly, the state space is shrunk by the most weight supported tree algorithm (MWST) at the initial establishment stage of BN. Next, I-ACO is designed to improve the structure of BN in order to study the fault propagation path accurately. Finally, the experiment is conducted from two different perspectives for the rationality of the well-trained BN's structure. An example of practical application for the propagation path model of typical faults on the A320 air turbine starter is given to verify the validity and feasibility of the proposed method.

关键词： Fault propagation path model air turbine starter Bayesian network ant colony optimization algorithm Bayesian information criterion

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Identification of constitutive parameters for fractional viscoelasticity

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2014年第1期19卷 311-322页

作者： Zhao Xiao Yang Haitian He Yiqian Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China

This paper develops a numerical model to identify constitutive parameters in the fractional viscoelastic field. An explicit semi-analytical numerical model and a finite difference (FD) method based numerical model are derived for solving the direct homogenous and regionally inhomogeneous fractional viscoelastic problems, respectively. A continuous ant colony optimization (ACO) algorithm is employed to solve the inverse problem of identification. The feasibility of the proposed approach is illustrated via the numerical verification of a two-dimensional identification problem formulated by the fractional Kelvin-Voigt model, and the noisy data and regional inhomogeneity etc. are taken into account. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Parameters identification Inverse problem Viscoelasticity Fractional derivatives ant colony optimization algorithm

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Uncertain Multi-Objective Hazardous Materials Transport Route Planning Considering Resilience and Low-Carbon

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IEEE ACCESS 2023年 11卷 26921-26931页

作者： Wang, Zhanzhong Wang, Yan Jiao, Yuling Jilin Univ Transportat Coll Changchun 130022 Peoples R China

Hazardous material transport accidents are events with a low probability and high consequence risk. With an increase in the proportion of hazardous materials transported on domestic roads, an increasing number of scholars have begun to study this field. In this study, a multi-objective hazardous materials transport route planning model considering road traffic resilience and low carbon, which considers the uncertainty of demand and time and is under the limit of the time window. It transports many types of hazardous materials from multiple suppliers to multiple retails with three goals (transportation cost, risk, and carbon emission). This model fills the gap in the research on hazardous material transportation in the field of low carbon, and this is the first time that road traffic resilience is considered in the transport of hazardous materials as one of the weight factors of risk calculation. We designed a improved ant colony optimization algorithm (ACO) to obtain the pareto optimal solution set. We compared the improved ACO with genetic algorithm and simulated annealing algorithm. The results show that the improved ACO has better solution quality and space, which verifies the validity and reliability of the improved ACO.

关键词： Hazardous materials Road traffic Resilience Transportation Path planning Carbon dioxide Accidents Routing Hazardous materials transportation uncertainty theory ant colony optimization algorithm resilience multi-objective

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Path Planning for Autonomous Underwater Vehicles Under the Influence of Ocean Currents Based on a Fusion Heuristic algorithm

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第9期70卷 8529-8544页

作者： Wen, Jiabao Yang, Jiachen Wang, Tianying Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

Recently, research on path planning for the autonomous underwater vehicles (AUVs) has developed rapidly. Heuristic algorithms have been widely used to plan a path for AUV, but most traditional heuristic algorithms are facing two problems, one is slow convergence speed, the other is premature convergence. To solve the above problems, this paper proposes a new heuristic algorithms fusion, which improves the genetic algorithm with the ant colony optimization algorithm and the simulated annealing algorithm. In addition, to accelerate convergence and expand the search space of the algorithm, some algorithms like trying to cross, path self-smoothing and probability of genetic operation adjust adaptively are proposed. The advantages of the proposed algorithm are reflected through simulated comparative experiments. Besides, this paper proposes an ocean current model and a kinematics model to solve the problem of AUV path planning under the influence of ocean currents.

关键词： Heuristic algorithms Path planning Oceans Convergence Clustering algorithms Genetic algorithms Simulated annealing Genetic algorithm ant colony optimization algorithm autonomous underwater vehicle path planning heuristic algorithms fusion

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Integrated fault location method for distribution networks based on IACO-PS

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JOURNAL OF POWER ELECTRONICS 2023年第1期23卷 112-126页

作者： Zhang, Shuqing Zhang, Xiaowen Jiang, Anqi Zhang, Liguo Li, Mingliang Yanshan Univ Inst Elect Engn Qinhuangdao Hebei Peoples R China

This paper develops a new hybrid method based on an improved ant colony optimization algorithm that incorporates pattern search (IACO-PS) for determining the location of faults in a distribution network. The performance of the conventional ant colony optimization (ACO) algorithm is improved using the opposite-based learning strategy to generate the initial population and adding a weight coefficient into the pheromone update mechanism to dynamically adjust the pheromone volatilization factor. The hybrid IACO-PS algorithm combines the individual strengths of ACO and PS. In addition, the fitness function is constructed by counting the false and missing fault information into the fault variable. In optimizing benchmark function experiments, the proposed hybrid IACO-PS presents a superior performance when compared to other improved versions of ACO. The effectiveness of the proposed approach is corroborated by tests performed on an IEEE 134-bus network. Simulation results show that the proposed hybrid IACO-PS method can determine the location of a fault even in the presence of fault distortion. In addition, it is immune to noise and data loss errors. Finally, the method proposed in this paper significantly outperforms other published fault location methods, and it can accurately locate faults and identify the type of distortion.

关键词： Fault location Distribution network ant colony optimization algorithm Pattern search

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