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Hybrid ant colony optimization algorithm for Solving the Open Vehicle Routing Problem

电脑学刊 2016年第4期27卷 41-54页

作者： Bin Ge Yue Han Chen Bian

Open vehicle routing problem (OVRP) is a hot research topic in modern operational research, compared with classical VRP problems, one of its marked characteristics is that the vehicle can choose the other distribution center as an end after the completion of the transportation service. The solving goal of OVRP is to build a Hamiltonian path to meet the needs of all customers. In order to solve the OVRP, a hybrid ant colony optimization (HACO) algorithm based on random distribution of loading and dynamically encoding was proposed. Firstly, the initial solutions were obtained through the method of random loading, and the colony optimization algorithm was adopted to get the optimal solution. Then the optimal solution was encoded as the zeroth particle of particle swarm algorithm. The initial fitness values were regarded as the historical optimal solution for individual. In order to get the best historical of individual and global optimal solution, the global optimal solution and the switching sequence of each particle was calculated and implemented, combining the hill climbing strategy for local search with side step. Computer simulations on the benchmark problems show that it can quickly and effectively get the known optimal solution or approximate solution.

关键词： ant colony optimization algorithm dynamic coding open vehicle routing problem particle exchange sequence random stowage

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Hybrid ANFIS with ant colony optimization algorithm for prediction of shear wave velocity from a carbonate reservoir in Iran

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International Journal of Mining and Geo-Engineering 2016年第2期50卷 231-238页

作者： H. Fattahi H. Nazari A. Molaghab

Shear wave velocity (V_s) data are key information for petro-physical, geophysical and geomechanical studies. Although compressional wave velocity (V_p) measurements are available in almost every well, shear wave velocity is usually not recorded for most of old wells due to the technological limitations. Furthermore, measurement of shear wave velocity comparatively costly. This study proposes a novel methodology to tackle these problems by taking advantage of Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) with ant colony optimization algorithm (ACO) based on Fuzzy C-Means Clustering (FCM) and Subtractive Clustering Method (SCM). The ACO is combined with two ANFIS models for determination of the optimal value of its user-defined parameters. The optimization implementation by the ACO significantly improves the generalization ability of the ANFIS models. These models are used in this study to formulate conventional well log data into Vs in a swift, economical, and accurate manner. A total of 3030 data points were used for model construction and 833 data points were employed for assessment of ANFIS models. Finally, a comparison among ANFIS models, and six well–known empirical correlations proved that ANFIS models can outperform the other methods. This strategy was successfully applied in the Marun reservoir, Iran.

关键词： Shear wave velocity Carbonate reservoir ANFIS-subtractive clustering ANFIS-fuzzy c-means clustering ant colony optimization algorithm

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Vehicle Routing Problem based Model and ant colony optimization algorithm for Group Charge Problem of Ingot Casting

Vehicle Routing Problem based Model and Ant Colony Optimizat...

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21st Chinese Control and Decision Conference

作者： Huang Hui Tu Nai-wei Ma Tian-mu Zheng Bing-lin Chai Tian-you Minist Educ Key Lab Proc Ind Automat Shenyang 110004 Peoples R China

ISBN: (纸本)9781424427239

Group charge problem of ingot casting is one of iron & steel production planning and scheduling problems. Compares with the steel-making - continuous casting's group charge problem, the ingot casting's group charge problem has the difference in the process constraint and planning rules. On the basis of analyzing the difference between group charge problem of ingot casting and group charge problem of steelmaking, VRP model for group charge problem of ingot casting is established in consideration of technological regulation and process constraint. Aiming at characteristic of model, ant colony optimization (ACO) algorithm is proposed for solving the model. The experimental result using practical production data shows the proposed model and algorithm are effective and feasible.

关键词： ingot casting charge plan vehicle routing problem ant colony optimization algorithm

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A new ant colony optimization-based algorithm for range query answering problem in relational schema-based P2P database systems

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KNOWLEDGE AND INFORMATION SYSTEMS 2015年第3期43卷 719-749页

作者： Aghamahmoodi, Saeedeh Rankoohi, Seyed Mohammad Taghi Rouhani Aghamahmoodi, Fatemeh Shahid Beheshti Univ Elect & Comp Engn Dept GC Tehran Iran Nooretouba Virtual Univ Informat Technol Engn Dept GC Tehran Iran

Nowadays, peer-to-peer database systems (P2PDBSs) aiming at data sharing in the Web have become very popular. Due to the absence of global knowledge about data placement in unstructured P2P networks, query processing and answering is a challenging problem in such systems. This process is provided by query routing and is an optimization problem whose goal is to find the maximum results with spending a predetermined cost. With the aim of improving the efficiency of range query answering algorithm in relational schema-based P2PDBSs, the present study, for the first time, adapts the ant colony metaphor with range query answering problem in relational schema-based P2P systems and proposes a new algorithm using the ant colony optimization approach in which the researchers use histogram data structure and apply both positive and negative feedbacks, dynamic learning rate, and local heuristic mechanisms and show that the proposed algorithm gives better results than the comparative greedy-based method. The experimental tests indicate that in the best case, the average number of traveled links for finding one answer (i.e., cost-to-answers ratio) is decreased almost by half in contrast to that of greedy-based algorithm. Furthermore, the achieved results indicate that the proposed algorithm is completely flexible with the users' requests, i.e., more query answers or less query response time, and the algorithm parameters can be properly set to meet the users' requests.

关键词： Unstructured P2P network Relational schema-based P2P database system Query answering problem Range query routing ant colony optimization algorithm Greedy algorithm

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Research on Path Planning of AGV Based on Improved ant colony optimization algorithm

Research on Path Planning of AGV Based on Improved Ant Colon...

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第33届中国控制与决策会议

作者： Jiuxiang Sun Ya'nan Yu Ling Xin College of Automation and Electronic Engineering Qingdao University of Science and Technology

Path planning is a key problem in the motion control of mobile *** order to solve the problem that the traditional storage mode of automatic container terminal affects the overall operation efficiency,this paper puts forward a matrix yard storage mode,which is transformed into grid map model,and then uses ant colony optimization algorithm to plan the path of *** at the shortcomings of traditional ant colony optimization algorithm(ACO) in global path planning,such as slow convergence speed and weak optimization ability,an improved ant colony path planning algorithm is ***,the grid map is established,and the fruit fly optimization algorithm(FOA) is used for fast pre-search on the grid map to generate the original pheromone distribution required by the ant colony optimization algorithm,and then the ant colony optimization algorithm is used for global path *** the same time,in order to solve the problem of many path turning angles and large cumulative turning angles in the planning,the path smoothing is carried *** simulation results show that the improved algorithm has fewer turns and smoother path,and the improved ant colony algorithm has a greater improvement in path search speed and accuracy than the traditional algorithm.

关键词： ant colony optimization algorithm Fruit fly optimization algorithm matrix yard storage mode Smoothing

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Wireless Networked Control Systems with neural networks and ant colony optimization algorithm

Wireless Networked Control Systems with neural networks and ...

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第37届中国控制会议

作者： Qiang Xu Jia Yang College of Computer Science and Information Engineering Chongqing Technology and Business University School of Electrical and Electronics Engineering Chongqing University of Technology

Due to Wireless network delay and large delay time of the controlled plant,the performance of the wireless networked control systems（WNCS） is *** order to effectively eliminate the influence of network delay and large delay time,a novel structure of the WNCS is proposed that new Smith predictor combined with neural network prediction and identification *** system’s delay time must be well *** abrupt mutation result from the training error sum square of the real output and the expected output of the neural network was used to identify the delay *** input sample period of the neural network was changed so that it could discriminate the delay time of the nonlinear *** discriminated large timedelay was applied to neural network prediction *** errors between input and prediction model output were used to search PID controller parameters based on ant colony optimization *** method was applied to control boiler combustion *** simulation results show that this scheme has much better advantage of celerity and *** is the paper style requirement for the Chinese Control *** writers of papers should and must provide normalized electronic documents in order for readers to search and read papers conveniently.

关键词： Large delay time Neural network prediction model ant colony optimization algorithm Wireless networked control systems(WNCS) Network delay Smith predictor

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Application of the Hybrid algorithm Combining ant colony optimization algorithm with Microgenetic algorithm to the optimization of Multilayered Radar Absorbing Coating

Application of the Hybrid Algorithm Combining Ant Colony Opt...

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2008 International Conference on Microwave and Millimeter Wave Technology(2008国际微波毫米波技术会议)

作者： Kun Chao Yunlin Liu Rugui Yang Electromagnetics Institute Southwest Jiaotong University Chengdu 610031China

A new optimization technique based on the hybrid algorithm combining ant colony optimization algorithm with microgenetic algorithm is presented for the design of muitilayered radar absorbing materials. During the optimization procedure the optimization constrained conditions are different in order to meet the practical requirementsInthe different frequency bands between 2 GHz and 18 GHz,and the muitilayered radar absorbing materials is also designed for a given maximum total thickness. The effects of the thickness and the number of layers on the optimization results are discussedIndetail. The numerical results show thatThis new hybrid algorithm can obtain a better solution than that of the genetic algorithm.

关键词： Index Terms-multilayered radar absorbing materials hybrid algorithm ant colony optimization algorithm microgenetic algorithm

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Grid Task Scheduling Strategy Based on Particle Swarm optimizationand ant colony optimization algorithm

Grid Task Scheduling Strategy Based on Particle Swarm Optimi...

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2010 International Conference on Advanced Measurement and Test (AMT 2010)

作者： Wei Pengcheng 1,2,a,Shi xi 2,b 1 Computer Science and Engineering College,Chongqing University,Chongqing,China 2 Department of Computer Science,Chongqing Education College Chongqing,China

Based on particle swarm optimization algorithm,this paper presents a grid scheduling optimization algorithm combing the advantages of ant colony optimization *** algorithm processes task scheduling through particle swarm optimization algorithm to get a group of relatively optimal solutions,and then conducts small-area local search with ant colony optimization *** analysis and results of the simulation experiments show that this scheduling algorithm effectively achieves load balancing of resources with comprehensive advantages in time efficiency and solution accuracy compared to the traditional ant colony optimization algorithm and particle swarm optimizationalgorithm,and can be applied to task scheduling in grid computing.

关键词： grid computing task scheduling particle swarm optimization algorithm ant colony optimization algorithm

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STUDY ON CLOUD RESOURCE ALLOCATION STRATEGY BASED ON PARTICLE SWARM ant colony optimization algorithm

STUDY ON CLOUD RESOURCE ALLOCATION STRATEGY BASED ON PARTICL...

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2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems

作者： Zhengqiu Yang Meiling Liu Jiapeng Xiu Chen Liu School Of Software Engineering Beijing University of Posts and Telecommunications School Of Computer Science Beijing University of Posts and Telecommunications

Cloud computing environment for the efficient resources allocation is an important issue in the field of cloud computing. The resources in Cloud computing application platform are distributed widely and with great diversity. User demands of real-time dynamic change are very difficult to predict accurately. The heuristic ant colony algorithm could be used to solve this kind of problems, but the algorithm has slow convergence speed and parameter selection problems. Aiming at this problem, this paper proposes an optimized ant colony algorithm based on particle swarm to solve cloud computing environment resources allocation problem.

关键词： Particle swarm optimization algorithm ant colony optimization algorithm Cloud resource allocation

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A Multi-dimensional ant colony optimization algorithm for Multi-objective Multi-compartment Vehicle Routing Problem with Time Windows

A Multi-dimensional Ant Colony Optimization Algorithm for Mu...

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第33届中国过程控制会议

作者： Ning Guo Qiu-Yi Shen Bin Qian Jing Na Rong Hu Academy of Information Engineering and Automation Kunming University of Science and Technology Academy of Mechanical and Electrical Engineering Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology

In this paper,a multi-dimensional ant colony optimization(MDACO) algorithm is proposed to solve the multi-objective multi-compartment vehicle routing problem(MOMCVRP) with time windows(MOMCVRPTW).Minimizing the total travel distance and maximizing the customer satisfaction are the objectives that the MOMCVRPTW needs to *** the global search phase of MDACO,first,a multi-dimensional pheromone concentration matrix(MDPCM) is presented to preserve the information of excellent individuals,and second,two heuristic rules based on problem characteristics are developed to initialize the *** are helpful to enhance the ability of the algorithm to find high-quality regions in the solution *** the local search phase of MDACO,several types of neighborhood operations combined with the speed-up evaluation method and the variable neighborhood descent strategy are designed to deeply search the found high-quality ***,the simulation experiments and algorithm comparisons verify the effectiveness of the proposed MDACO.

关键词： ant colony optimization algorithm multi-objective optimization multi-compartment vehicle routing problem with time windows multi-dimensional pheromone concentration matrix speed-up evaluation variable neighborhood descent

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