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Parameters optimization of Synchronous Induction Coilgun Based on ant colony algorithm

IEEE TRANSACTIONS ON PLASMA SCIENCE 2011年第1期39卷 100-104页

作者： Liu, Wenbiao Cao Yanjie Zhang, Yuan Wang, Jie Yang, Dawei Naval Aeronaut & Astronaut Univ Yantai 264001 Shandong Peoples R China

Electrical-to-kinetic energy conversion efficiency of synchronous induction coilguns (SICG) is the main limiting factor of its development. In the system of SICG, the change of any electromagnetic parameters can directly or indirectly affects the electrical-to-kinetic energy conversion efficiency. In order to improve the electrical-to-kinetic energy conversion efficiency of SICG, the electromechanical model of SICG was built at first in this paper. Then, the structural parameters of SICG with 60-mm caliber were optimized with ant colony optimization algorithm. The electrical-to-kinetic energy conversion efficiency of the system was regarded as the target function, while the center-to-center space between the drive coil and the armature, and the structural parameters of the drive coil and the armature were regarded as variables. Results of research indicated that the electrical-to-kinetic energy conversion efficiency of the system was improved through parameter optimization. In order to validate the results of parameter optimization, an experiment was carried out with the first stage of SICG.

关键词： ant colony optimization algorithm armature drive coil energy conversion efficiency efficiency of synchronous induction coilguns (SICG)

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Web Spam Detection using Link-based ant colony optimization

Web Spam Detection using Link-based Ant Colony Optimization

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26th IEEE International Conference on Advanced Information Networking and Applications (AINA)

作者： Taweesiriwate, Apichat Manaskasemsak, Bundit Rungsawang, Arnon Kasetsart Univ Fac Engn Dept Comp Engn Mass Informat & Knowledge Engn Lab Bangkok 10900 Thailand

ISBN: (纸本)9780769546513

Web spam is one of the most important problems which degrade quality and efficiency of web search engines. In this paper, we present a novel link-based ant colony optimization learning algorithm for spam host detection. The host graph is first constructed by aggregating pages' hyperlink structure. Following the TrustRank assumption, ants start walking from a normal host and randomly follow host links with a probability distribution. Then, the classification rules are appropriately generated according to common features of normal hosts sequentially discovered by ants. From the experiments with the WEBSPAM-UK2006 dataset, the proposed learning model provides much accuracy in classifying both normal and spam hosts than several baselines, including a state of the art C4.5. Moreover, we also provide an analysis in parameter tuning for better results.

关键词： link spam content spam web spam spam detection ant colony optimization algorithm web link structure

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SAR Image Ship Detection Based on ant colony optimization

SAR Image Ship Detection Based on Ant Colony Optimization

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5th International Congress on Image and Signal Processing (CISP)

作者： Li, Lin-lin Wang, Ji-kui Shandong Xiehe Coll Sch Mech & Elect Engn Jinan Peoples R China

ISBN: (纸本)9781467309646

Edge detection is an important method of extracting image edge, which occupies an important position in ship detection. In this article, ant colony optimization algorithm is adopted, which adjusts threshold on edge detection dynamically. By the contrast analysis with traditional edge detection operators and WT method, ant colony optimization algorithm can reduce the detection computing time and cost greatly, extracts the ship targets in SAR images effectively, keeps the ship structure completely, and ensures accuracy of the test results. ant colony optimization algorithm has a lot of advantages in dealing with image edge detection and so on discrete optimization problems, which has very broad prospects in image processing.

关键词： ant colony algorithm ant colony optimization algorithm edge detection ship detection

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Identification and Control optimization algorithm based on Neural Networks and ant colony

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International Conference on Computational Materials Science (CMS 2011)

作者： Yang, Jia Xu, Qiang Yu, ChengBo Chongqing Univ Technol Coll Elect Informat & Automat Chongqing 400050 Peoples R China Chongqing Univ Coll Comp Sci & Informat Engn Chongqing Peoples R China

ISBN: (纸本)9783037851555

It is difficult to have good performance to control large delay time system. A neural network identification method for nonlinear system's delay time was discussed. Using the abrupt mutation resulted from the training error sum square of the real output and the expected output of the network, this method changed the input sample period of the neural network so that it could discriminate the delay time of the nonlinear model. Combining the discrimination of neural network system with long time delay and the control method based on model prediction, searching PID controller parameters based on ant colony optimization algorithm, it was applied to control boiler combustion system. The simulation results show that this scheme has much better advantage of celerity and robustness.

关键词： Large Delay Time System Identification of Delay Time Prediction Model ant colony optimization algorithm

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Parameters optimization of Synchronous Induction Coilgun Based on ant colony algorithm

Parameters Optimization of Synchronous Induction Coilgun Bas...

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15th International Symposium on Electromagnetic Launch (EML) Technology

作者： Liu, Wenbiao Cao Yanjie Zhang, Yuan Wang, Jie Yang, Dawei Naval Aeronaut & Astronaut Univ Yantai 264001 Shandong Peoples R China

关键词： ant colony optimization algorithm armature drive coil energy conversion efficiency efficiency of synchronous induction coilguns (SICG)

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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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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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Layout and size optimization of sanitary sewer network using intelligent ants

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ADVANCES IN ENGINEERING SOFTWARE 2012年 51卷 49-62页

作者： Moeini, R. Afshar, M. H. Iran Univ Sci & Technol Sch Civil Engn Envirohydroinformat Ctr Excellence Tehran Iran

The incremental solution building capability of ant algorithm is exploited in this paper for the efficient layout and pipe size optimization of sanitary sewer network. Layout and pipe size optimization of sewer networks requires that the pipe locations, pipe diameters and pipe slopes are optimally determined. This problem is a highly constrained Mixed-Integer Nonlinear Programming (MINLP) problem presenting a challenge even to the modern heuristic search methods. In this paper, the ant colony optimization algorithm (ACOA) is equipped with a Tree Growing algorithm (TGA) to efficiently solve the sewer network layout and size optimization problem and its performance is compared with the conventional application of the ACOA. The method is based on the assumption that a base layout including all possible links of the network is available. The TGA is used to construct feasible tree-like layouts out of the base layout defined for the sewer network, while the ACOA is used to optimally determine the pipe diameters of the constructed layout. An assumption of sewer flow at maximum allowable relative depth is made allowing for the calculation of the optimal pipe slopes in the absence of any pump and drop in the network. Two different formulations are, therefore, proposed and their performances are tested against hypothetical problems. In the first formulation, ACOA is used in a conventional manner for pipe size optimization while an ad hoc engineering concept for the layout determination. In the second formulation, however. ACOA equipped with TGA is used to simultaneously determine both the layout and pipe sizes of the network. Proposed formulations are used to solve three hypothetical test examples of different scales and the results are presented and compared. The results indicate the ability of the proposed method to optimally solve the problem of layout and size determination of sewer networks. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： ant colony optimization algorithm Tree Growing algorithm Sanitary sewer network Layout Pipe size Mixed-Integer Nonlinear Programming

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The Application of ant colony in Maintenance Combinatorial Analysis

The Application of Ant Colony in Maintenance Combinatorial A...

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International Conference on Emerging Computation and Information Technologies for Education (ECICE 2012)

作者： Jian, Xia Tongji Univ Coll Aerosp Engn & Appl Mech Shanghai 200092 Peoples R China

ISBN: (纸本)9783642284656

Maintenance disassembly sequence planning is an important part of maintainability design. To tackle disassembly sequence planning problem efficiently, based on the product's basic information and constraint relations between parts, disassembly Petri net reachable graph is presented. The problem of disassembly sequence planning is transformed into that of searching optimal paths in the graph. At the same time an ant colony optimization algorithm is presented to search the optimal solutions. Through examples verify the effectiveness of the method.

关键词： Disassembly sequence planning Reachable graph ant colony optimization algorithm

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Comparison of ABC and ACO algorithms Applied for Solving the Inverse Heat Conduction Problem

Comparison of ABC and ACO Algorithms Applied for Solving the...

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International Symposium on Swarm and Evolutionary Computation/ Symposium on Swarm Intelligence and Differential Evolution

作者： Hetmaniok, Edyta Slota, Damian Zielonka, Adam Witula, Roman Silesian Tech Univ Inst Math PL-44100 Gliwice Poland

ISBN: (纸本)9783642293528;9783642293535

In this paper we present the comparison of numerical methods applied for solving the inverse heat conduction problem in which two algorithms of swarm intelligence are used: Artificial Bee colony algorithm (ABC) and ant colony optimization algorithm (ACO). Both algorithms belong to the group of algorithms inspired by the behavior of swarms of insects and they are applied for minimizing the proper functional representing the crucial part of the method used for solving the inverse heat conduction problems. Methods applying the respective algorithms are compared with regard to their velocity and precision of the received results.

关键词： Swarm Intelligence Artificial Bee colony algorithm ant colony optimization algorithm Inverse Heat Conduction Problem

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