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作者： Yang Liu Jianwei Ma Shaofei Zang Yibo Min School of Information Engineering Henan University of Science and Technology

Aiming at the problem that the traditional ant colony algorithm(ACO) has poor solution quality in the dynamic path planning process, this paper proposes an improved ACO. Firstly, the genetic operator fused with the traditional ACO is proposed, and the genetic operation is used to expand the search space of the solution. Secondly, the fitness function is introduced in the traditional ACO and the safety distance is added. The pros and cons of the comprehensive evaluation algorithm planning path. Then, by introducing the optimization operator, the redundant nodes are eliminated and the smoothness is improved. Finally, the path planning simulation experiment is carried out in the grid map. The results show that the proposed algorithm can find a shorter and smoother in the dynamic environment path.

关键词： mobile robot Dynamic environment Path planning ant colony optimization algorithm

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Short-term load forecasting based on fuzzy neural network using ant colony optimization algorithm

Short-term load forecasting based on fuzzy neural network us...

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2016 International Conference on Automotive Engineering,Mechanical and Electrical Engineering(AEMEE 2016)

作者： Zhichao Ren Qian Chen Lipin Chen Kaijiang Cao Haiyan Wang Rundong Chen Yun Tian Yan Wang Haichao Wang Research Institute of Economics and Technology of State Grid Sichuan Electric Power Company Beijing China-power Information Technology Co.Ltd. College of Economics and Management North China Electric Power University

On the basis of fuzzy neural network load forecasting, the weights and thresholds of fuzzy neural network and the number of neurons in hidden layer are optimized by ant colony algorithm. A short-term load forecasting model of fuzzy neural network based on ant colony optimization is proposed. Through case analysis, it shows that this algorithm can obtain high accuracy, and it is an effective method for short-term load forecasting.

关键词： ant colony optimization algorithm Fuzzy neural network Short-term load forecasting Parameter optimization

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A Logistic Distribution Routes Solving Strategy Based on the Physarum Network and ant colony optimization algorithm

A Logistic Distribution Routes Solving Strategy Based on the...

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IEEE International Symposium on Cyberspace Safety and Security

作者： Qianguo Chen Tao Qian Kui Liu Center for Teaching and Learning Development Chongqing Technology and Business University Software Development Center Bank of Chongqing Experimental Teaching Center of International Studies Southwest University

ISBN: (纸本)9781479989386

The Physarum Network with single inlet and multi outlet model (SMPN) exhibits a unique feature that the critical pipelines are reserved with the evolution of network. In addition, ant colony optimization algorithm is a classic optimization algorithm of simulated evolutionary algorithms, which has been used to solve optimal scheduling problems. In this paper, drawing on this feature, an optimized ant colony optimization (ACO) algorithm denoted as SMPNACO algorithm is proposed based on the Physarum Network and ant colony optimization algorithm (ACO) to solve the Vehicle Routing Problem (VRP). Throughout the algorithm, the amount of pheromone flowed in network are related to the customers' requirement. When the pheromone matrix is updated, the SMPNACO algorithm updates both the pheromone released by ants and the flowing pheromone in the Physarum Network. By adding extra pheromones in the Physarum Network improves the convergence performance of ant colony optimization algorithm. The simulative experiments show that the SMPNACO algorithm is less affected by the initial total pheromone, this algorithm is feasible in solving the small scale VRP, and can effectively solve the VRP.

关键词： Physarum Network model ant colony optimization algorithm Logistics distribution routes optimization VRP

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A routing cooperative selection method based on ant colony optimization algorithm

A routing cooperative selection method based on ant colony o...

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International Conference on Cyberspace Technology (CCT 2014)

作者： Sun Dandan Zhang Hongxin School of Electronic Engineering Beijing University of Posts and Telecommunications 100876 China Sch. of Electron. Eng. Beijing Univ. of Posts & Telecommun. Beijing China

ISBN: (纸本)9781510802704

Cooperative routing selection algorithm is based on the limited bandwidth and collaborative remain energy, according to pheromone concentration in ant colony optimization algorithm. The ant colony optimization algorithm is applied to improving collaboration network routing problems to reduce network congestion and reduce node energy consumption. Simulation demonstrate that the algorithm obtains a global optimization and makes an average 60% reduction in end to end delay, increased the utilization of network resources.

关键词： ant colony optimization algorithm Pheromone density The Cooperative routing ant algorithm ant colony optimization Network routing Web resources optimization algorithms Network Congestion selection algorithm Global optimization algorithms selection methods node energy

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A hybrid ant colony optimization algorithm based on Map Reduce

A hybrid ant colony optimization algorithm based on Map Redu...

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2016 3rd International Conference on Materials Engineering,Manufacturing Technology and Control(ICMEMTC 2016)

作者： Ming Cai Yongan Zuo School of Internet of Things Engineering Jiangnan University

The traditional ant colony optimization（ACO） had defects in long searching time and convergence in local optima solution. To solve the problem, a hybrid ant colony optimization algorithm（HACO） was proposed in this paper. Two modes in HACO are defined that are Default Model and Elite Model. In order to search the optimum result, the two modes will be automatically switched in this algorithm. It also use the parallel calculation model of Map Reduce to loop iteration part of ACO and deploy it on the Hadoop cloud computing platform. Finally, simulation results validate the proposed approach on the traveling salesman problem.

关键词： Map Reduce ant colony optimization algorithm Hadoop Traveling salesman problem hybrid ACO

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Dynamic Path Planning of Mobile Robot Based on Improved ant colony optimization algorithm 2019

Dynamic Path Planning of Mobile Robot Based on Improved Ant ...

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Proceedings of the 2019 8th International Conference on Networks, Communication and Computing

作者： Yang Liu Jianwei Ma Shaofei Zang Yibo Min School of Information Engineering Henan University of Science and Technology Luoyang China

ISBN: (纸本)9781450377027

Aiming at the problem that the traditional ant colony algorithm (ACO) has poor solution quality in the dynamic path planning process, this paper proposes an improved ACO. Firstly, the genetic operator fused with the traditional ACO is proposed, and the genetic operation is used to expand the search space of the solution. Secondly, the fitness function is introduced in the traditional ACO and the safety distance is added. The pros and cons of the comprehensive evaluation algorithm planning path. Then, by introducing the optimization operator, the redundant nodes are eliminated and the smoothness is improved. Finally, the path planning simulation experiment is carried out in the grid map. The results show that the proposed algorithm can find a shorter and smoother in the dynamic environment path.

关键词： ant colony optimization algorithm Path planning mobile robot Dynamic environment

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Self-healing Restoration of a Distribution System Using Hybrid Fuzzy Control/ant-colony optimization algorithm

Self-healing Restoration of a Distribution System Using Hybr...

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3rd International Conference on Electric Power and Energy Conversion Systems (EPECS)

作者： Abd El-Hamed, Mohamed Zaki El-Khattam, Walid El-Sharkawy, Rania Arab Acad Sci & Technol Cairo Egypt Ain Shams Univ Cairo Egypt Arab Acad Sci & Technol Cairo Egypt

ISBN: (纸本)9781479906871;9781479906888

The restoration process for distribution system grid needs the operation of line switching to restore as many loads as possible for the faulted area. In this paper a multi objective, multi constraint combinatorial optimization problem is formulated to solve the self-healing restoration problem. A hybrid Fuzzy Control (FC)-ant colony optimization algorithm (ACOA) is proposed using values for P, Q from a SCADA system. Two case studies without/with distributed generation are carried out to evaluate the effectiveness and speed of the proposed algorithm. The obtained results are compared with the conventional ACO to illustrate the accuracy of the proposed hybrid algorithm. Finally, conclusions are discussed.

关键词： ant colony optimization algorithm Fuzzy Control Power System Restoration

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Application of the TSP based on ant colony optimization algorithm in WebGIS

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Wuhan Ligong Daxue Xuebao/Journal of Wuhan University of Technology 2008年第11期30卷 162-164页

作者： Xue, Sheng-Jun Hu, Jun-Jie Wu, Jun School of Computer Science and Technology Wuhan University of Technology Wuhan 430063 China

To solve the traveling salesman problem (TSP), the application of ant colony optimization algorithm (ACO) based on the web geographical information system (WebGIS) is presented. In order to improve the performance of optimization, the proposed algorithm adopts a kind of spatial topology structure combined with the ACO and treats 2-opt as a local searching strategy. Given the certain custom number, the algorithm can obtain the preferable global solving result. Compared with other two algorithms-the genetic algorithm and simulated annealing algorithm, the ACO algorithm based on the WebGIS can make the result converge to the global optimum faster and has higher accuracy. The algorithm can also be extended to solve other correlative combination optimization problems. Experimental results indicate the validity of the proposed algorithm.

关键词： ant colony optimization algorithm Traveling salesman problem TSP WebGIS

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New ant colony optimization algorithm for Stochastic Loader Problem

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Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice 2006年第8期26卷 109-115页

作者： Zhao, Pei-Xin Ma, Jian-Hua Zhao, Bing-Xin Management School Shandong University Ji'nan 250100 China Information Management College Shandong Economic University Ji'nan 250014 China

The stochastic loader problem and the procedure for solutions were proposed in this paper. On the basis of basic ant colony optimization algorithm, the new ant colony optimization algorithm with inner and outer mutation was designed to solve this problem. Two numerical examples were provided to illustrate the efficiency and reliability of this new algorithm.

关键词： ant colony optimization algorithm Combinatorial optimization Loader problem Stochastic

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A jump point search improved ant colony hybrid optimization algorithm for path planning of mobile robot

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2022年第5期19卷

作者： Chen, Tao Chen, Suifan Zhang, Kuoran Qiu, Guoting Li, Qipeng Chen, Xinmin Zhejiang Univ Sci & Technol Coll Mech & Energy Engn 318 Liuhe Rd Hangzhou 310023 Peoples R China Chinese Acad Sci Ningbo Inst Mat Technol & Engn 1219 Zhongguan West Rd Ningbo 315201 Peoples R China

To improve the finding path accuracy of the ant colony algorithm and reduce the number of turns, a jump point search improved ant colony optimization hybrid algorithm has been proposed in this article. Firstly, the initial pheromone concentration distribution gets from the jump points has been introduced to guide the algorithm in finding the way, thus accelerating the early iteration speed. The turning cost factor in the heuristic function has been designed to improve the smoothness of the path. Finally, the adaptive reward and punishment factor, and the Max-Min ant system have been introduced to improve the iterative speed and global search ability of the algorithm. Simulation through three maps of different scales is carried out. Furthermore, the results prove that the jump point search improved ant colony optimization hybrid algorithm performs effectively in finding path accuracy and reducing the number of turns.

关键词： Mobile robot path planning jump point search algorithm ant colony optimization algorithm turn cost factor

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