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An Improved ant colony optimization with Optimal Search Library for Solving the Traveling Salesman Problem

JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2015年第7期12卷 1440-1444页

作者： Pang, Shanchen Ma, Tongmao Liu, Ting China Univ Petr Coll Comp & Commun Engn Qingdao 266580 Shandong Peoples R China Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao 266590 Shandong Peoples R China

ant colony optimization algorithm is a kind of heuristic algorithm, which has been widely applied to solve the problem such as TSP (traveling salesman problem). This paper uses the proximity to construct the optimal search library. Based on the idea, an improved ant colony optimization algorithm is put forward. We also define the new heuristic information and the improved pheromone update rules. Thus an improved ant colony optimization algorithm is put forward that provides a better solution to the combinatorial optimization of MTSP (multiple traveling salesman problem) problem with E-mail traffic constraints.

关键词： Optimal Search Library ant colony optimization algorithm Heuristic Information The Improved Pheromone Update MTSP Problem

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optimization of renewable energy-based transmission system: an application of the genetic, ant colony optimization, Q-learning, and Dijkstra's algorithms

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INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION 2024年第6期44卷 386-405页

作者： Ansari, Sam Bansal, Ramesh C. C. Elsahar, Yasmin Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa Loughborough Univ Wolfson Sch Mech Elect & Mfg Engn Loughborough Leics England

Losses in the electrical power transmission and distribution systems are considered two of the most critical challenges in power grids. Reducing the related losses plays a significant role in increasing system efficiency in addition to diminishing costs. Therefore, optimum power transfer as well as finding a convenient route, are essential factors in electrical grids. This paper intends to substantially reduce the transmission/distribution-related losses by finding the shortest and most optimal path between the renewable energy power plant (producer) and the substations/consumers. A genetic algorithm (GA) is proposed for optimal routing to increase the system's reliability and minimize the losses of the entire network. In this work, by presenting a coding with chromosomes of variable length and considering the construction costs and the power transmission line/path as the fitness function, the appropriate route is obtained. The efficiency of the proposed method is compared with Dijkstra's algorithm, one of the conventional graph search approaches. The ant colony optimization (ACO) algorithm and a reinforcement learning algorithm, namely the Q-learning model, are employed to further explore the optimization efficiency of the proposed renewable energy-based transmission system. The simulation results demonstrate that the proposed models accurately determine the optimal pathway within an excellent time.

关键词： ant colony optimization algorithm Dijkstra's algorithm efficient power transmission genetic algorithm optimization reinforcement learning algorithm renewable energy

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Development and Control of an upper Limb Rehabilitation Robot via ant colony optimization-PID and Fuzzy-PID Controllers

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INTERNATIONAL JOURNAL OF ENGINEERING 2022年第8期35卷

作者： Mirrashid, N. Alibeiki, E. Rakhtala, S. M. Islamic Azad Univ Dept Elect Engn Aliabad Katoul Branch Aliabad Katoul Iran Golestan Univ Fac Engn Dept Elect Engn Gorgan Golestan Iran

The control of movement rehabilitation robots is necessary for the recovery of physically disabled patients and is an interesting open problem. This paper presents a mathematical model of the upper limb rehabilitation robot using Euler-Lagrange approach. Since the PID controller is one of the most popular feedback controllers in the control strategy due to its simplicity, we an ACO-PID controller for an upper limb rehabilitation robot. The main part of designing the PID controller is determining the gains of the controller. For this purpose. we ant colony optimization algorithm (ACO) to tune the coefficients. To evaluate the validity of the proposed controller, we have compared it to Fuzzy-PID controller and the PID controller adjusted with the Ziegler-Nichols method (ZN-PID). The results that the performance of the ACO-PID controller is better than the others. Also, the adaptive PID controllers (ACO-PID and Fuzzy-PID) ensure accurate tracking, finite-time convergence, and stability. The results that the mean absolute error and normalized root mean square (NRMS) of tracking error using the CO-PID are less than that using the Fuzzy-PID and ZN-PID controller.

关键词： Rehabilitation Robot ant colony optimization algorithm Fuzzy-PID Controller Ziegler-Nichols Method PID Tuning

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Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing

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Digital Communications and Networks 2023年第2期9卷 473-482页

作者： Jiabao Wen Jiachen Yang Tianying Wang Yang Li Zhihan Lv School of Electrical and Information Engineering Tianjin UniversityTianjin300072China College of Mechanical and Electrical Engineering Shihezi UniversityXinjiang832003China School of Data Science and Software Engineering Qingdao UniversityQingdao266000China

To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge *** Sensor Network(WSN)is a typical application of parallel *** achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be ***,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the *** paper proposes a task model and a cluster-based WSN model in edge *** our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more *** we propose a task allocation algorithm that combines the Genetic algorithm(GA)and the ant colony optimization(ACO)*** algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be *** experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.

关键词： Wireless sensor network Parallel computation Task allocation Genetic algorithm ant colony optimization algorithm Energy-efficient Load balancing

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The Lobe Fissure Tracking by the Modified ant colony optimization Framework in CT Images

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algorithmS 2014年第4期7卷 635-649页

作者： Chen, Chii-Jen Wang, You-Wei Shen, Wei-Chih Chen, Chih-Yi Fang, Wen-Pinn Yuanpei Univ Med Technol Dept Comp Sci & Informat Engn Hsinchu 30015 Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 10617 Taiwan Asia Univ Dept Comp Sci & Informat Engn Taichung 40402 Taiwan Chung Shan Med Univ Inst Med Taichung 40201 Taiwan

Chest computed tomography (CT) is the most commonly used technique for the inspection of lung lesions. However, the lobe fissures in lung CT is still difficult to observe owing to its imaging structure. Therefore, in this paper, we aimed to develop an efficient tracking framework to extract the lobe fissures by the proposed modified ant colony optimization (ACO) algorithm. We used the method of increasing the consistency of pheromone on lobe fissure to improve the accuracy of path tracking. In order to validate the proposed system, we had tested our method in a database from 15 lung patients. In the experiment, the quantitative assessment shows that the proposed ACO method achieved the average F-measures of 80.9% and 82.84% in left and right lungs, respectively. The experiments indicate our method results more satisfied performance, and can help investigators detect lung lesion for further examination.

关键词： CT ant colony optimization algorithm lung lobe fissure segmentation

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An ant stochastic decision based particle filter and its convergence

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SIGNAL PROCESSING 2010年第9期90卷 2731-2748页

作者： Xu, Benlian Zhu, Jihong Xu, Huigang Changshu Inst Technol Sch Elect & Automat Engn Changshu 215500 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

Particle filter (PF) is a kind of flexible and powerful sequential Monte-Carlo technique designed to solve the optimal nonlinear parameter estimation numerically, and the degradation of particles in generic PF occurs when it is applied to the model switching dynamic system. To avoid this phenomenon, an ant stochastic decision based particle filter is proposed to encapsulate model switching information through dividing probabilistically particles into two model operations, and then a well defined re-sampling scheme is introduced to gain a better overlap with the true density function. To show the theoretic consistency with the generic PF, its basic convergence result is presented as well. Finally, we compare the performance of our proposed algorithm with that of other estimators (e.g., PF and moving ant estimator), and simulation results demonstrate its superior robustness of parameter estimation for switching dynamic system. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Particle filter ant colony optimization algorithm Target tracking Convergence analysis

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ACO-HCO: Heuristic Performance Tuning Scheme for the Hadoop MapReduce Architecture

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JOURNAL OF INTERNET TECHNOLOGY 2020年第4期21卷 1151-1159页

作者： Liu, Chiang-Lung Lo, Hsiang-Fu Lee, Wei-Tsong Natl Def Univ Dept Elect & Elect Engn Chung Cheng Inst Technol Taoyuan Taiwan Chung Shan Inst Sci & Technol Chem Syst Res Div Taoyuan Taiwan Tamkang Univ Dept Elect Engn New Taipei Taiwan

Hadoop MapReduce is a widely-used cloud computing technology for big data processing. However, the Hadoop configuration parameters settings can significantly change the execution performance. Manual adjustment of the Hadoop parameters will be a time consuming and difficult task. In this paper, we propose ACO-HCO, a Hadoop configuration tuning scheme for MapReduce applications. We use MapReduce applications job history records to generate specific job profiles. Based on these profiles, an objective function for execution time is constructed with gene expression programming algorithm by mining the correlation among the core Hadoop configuration parameters and input data size. Leveraging the objective function, an ACO-based configuration optimizer is able to heuristically search for the optimal configuration for a given application. Experimental results show that ACO-HCO enhances the performance of Hadoop significantly compared with the default configuration. Moreover, ACO-HCO performs better than heuristic approach and the cost-based model in Hadoop performance tuning.

关键词： Hadoop performance tuning ant colony optimization algorithm Gene expression programming

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optimization of multiple autonomous underwater vehicle cooperative system communication network topology based on total energy consumption

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART M-JOURNAL OF ENGINEERING FOR THE MARITIME ENVIRONMENT 2020年第3期234卷 668-675页

作者： Liang, Qingwei Ou, Junlin Shi, Liang Zhang, Xin Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Shaanxi Peoples R China

Given the importance of the communication energy consumption of multiple autonomous underwater vehicle cooperative systems in practical work, this work optimizes the network topology to reduce total energy consumption. In accordance with the characteristics of underwater communication, the energy consumption of communication links is obtained, thereby obtaining the total communication energy consumption of multiple autonomous underwater vehicle cooperative systems. Taking the all-terminal reliability of the communication network as a constraint and the total energy consumption of network communication as the optimization goal, this work puts forward an optimization model for the communication network topology of multiple autonomous underwater vehicle cooperative systems. Furthermore, this work creatively describes the network topology optimization problem as a special path optimization problem suitable for the ant colony optimization algorithm presented to solve the optimization problem and shown to be effective and efficiency on this problem.

关键词： Network topology total energy consumption ant colony optimization algorithm optimization model multiple autonomous underwater vehicle cooperative system

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An improved dynamic model identification method for small unmanned helicopter

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2024年第1期96卷 175-183页

作者： Zhou, Jian Liu, Shuyu Lu, Jian Liu, Xinyu Xian Polytech Univ Sch Elect & Informat Xian Peoples R China

PurposeThe purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy's method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification ***/methodology/approachThis method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the "premature" phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal *** algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter ***/valueThis paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.

关键词： Small unmanned helicopter Frequency domain identification Frequency domain curve fitting ant colony optimization algorithm Levy method model identification

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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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