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检索条件"主题词=Ant Colony Optimization Algorithm"

共 307 条记录，以下是161-170 订阅

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An Improved ant colony optimization for VRP with Time Windows

An Improved Ant Colony Optimization for VRP with Time Window...

引用

International Conference on Information Technology and Management Innovation (ICITMI2012)

作者： Yu, Su-Ping Li, Ya-Ping Luoyang Inst Sci & Technol Comp & Informat Engn Dept Luoyang Peoples R China

ISBN: (纸本)9783037855744

The Vehicle Routing Problem (VRP) is an important problem occurring in many distribution systems, which is also defined as a family of different versions such as the Capacitated Vehicle Routing Problem (CVRP) and the Vehicle Routing Problem with Time Windows (VRPTW). The ant colony optimization (ACO) is a metaheuristic for combinatorial optimization problems. Given the ACO inadequacy, the vehicle routing optimization model is improved and the transfer of the algorithm in corresponding rules and the trajectory updated regulations is reset in this paper, which is called the Improved ant colony optimization (I-ACO). Compared to the calculated results with genetic algorithm (GA) and particle swarm optimization (PSO), the correctness of the model and algorithm is verified. Experimental results show that the I-ACO can quickly and effectively obtain the optimal solution of VRFTW.

关键词： Vehicle Routing Problem With Time Windows ant colony optimization algorithm Evenness of Solution ant Attracion

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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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Comparison of ant colony optimization and Artificial Bee colony algorithms for Solving Electronic Support Search Dwell Scheduling Problem 15

Comparison of Ant Colony Optimization and Artificial Bee Col...

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15th Turkish National Software Engineering Symposium (UYMS)

作者： Erkut, Okan Hardalac, Firat ASELSAN AS Facil Radar & Elect Warfare Syst Ankara Turkey Gazi Univ Dept Elect Elect Engn Ankara Turkey

ISBN: (纸本)9781665410700

Electronic support search dwell scheduling is a combinatorial optimization problem which appears when the dwells overlapping on frequency domain are assigned to different receivers and required to be executed simultaneously by the receivers that are on a single electronic support platform. Overlapping the dwells on time domain provides sensor fusion ability and improves the detection performance of the system against target radar systems. In this paper, dwell sets of the receivers are scheduled to solve electronic support search dwell scheduling problem with two meta-heuristic swarm intelligence algorithms: ant colony algorithm and Artificial Bee colony algorithm. Simulation results show that while both algorithms are capable of solving the problem, ant colony algorithm provides a solution roughly 43% faster with a limit on number of iterations and Artificial Bee colony algorithm provides roughly 30% quicker and 26% more consistent solution with a limit on maximum acceptable cost.

关键词： ant colony optimization algorithm Artificial Bee colony algorithm Combinatorial optimization Electronic Support Scheduling

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Traffic Flow Prediction Using SVR-ant colony optimization: A Practical Case of Tehran Highway

Traffic Flow Prediction Using SVR-Ant Colony Optimization: A...

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IEEE International Conference in Power Engineering Application (ICPEA)

作者： Zeynal, Hossein Zakaria, Zuhaina Kor, Ahmad Tehran Farayand Dena Engn Co Elect Engn Dept Tehran Iran Univ Teknol MARA UiTM Fac Elect Engn Shah Alam Selangor Malaysia Kharazm Power Engineers Co Engn Design Dept Tehran Iran

ISBN: (纸本)9781728185460

Traffic estimation is one of the most important issues in traffic control parlances. In this work, an integrated approach is proposed, which is a combination of three algorithms including K-means clustering, Support Vector Regression (SVR) and ant colony optimization (ACO) approach. Using the K-means clustering algorithm allows obtaining optimal values for SVR via ACO algorithm and then employ it to predict traffic flow. To carry out simulations, two realistic cases of traffic flow prediction for Tehran-Karaj and Tehran-Damavand highways is investigated at two checkpoints in the morning and afternoon periods. Further, to evaluate the quality of solutions obtained from the proposed method, a time series model was used to end comparisons. Based on the results, the NRMSE forecast error for the proposed method presents less as opposed to well-known SARIMA method for morning and evening periods. Therefore, the proposed method outperforms SARIMA in terms of prediction error;that is by 0.26 versus 0.31 and 0.11 versus 0.18 for Tehran-Karaj highway during the morning and evening intervals. According to the results for two main highways, the proposed method exhibits its suitability for practical application in traffic prediction with accurate solution and simplicity of application in real cases.

关键词： Traffic estimation Support Vector Regression ant colony optimization algorithm K-means algorithm SARIMA method

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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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Hybrid Quantum Approximate optimization Using Enhanced ant colony optimization to Solve Large-Scale Combinatorial optimization Problems 8

Hybrid Quantum Approximate Optimization Using Enhanced Ant C...

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8th International Conference on Soft Computing & Machine Intelligence (ISCMI)

作者： Ghimire, Bishad Mahmood, Ausif Elleithy, Khaled Univ Bridgeport Comp Sci & Engn Bridgeport CT 06604 USA

ISBN: (纸本)9781728186832

Quantum computing is a promising technology that may provide breakthrough solutions to today's difficult problems such as breaking encryption and solving large-scale combinatorial optimization problems. A class of algorithms referred to as Quantum Approximate optimization algorithm (QAOA) have been recently proposed. QAOA attempts to approximately solve hard problems using a protocol know as bang-bang. The technique is based on the unitary evolution using a Hamiltonian encoding of the objective function of the combinatorial optimization problem. QAOA has been explored in the context of several optimization problems such as Max-Cut problem, variational Eigenvalue problem etc. Recently, attempts have been made to create QAOA for the Traveling Salesman Problem (TSP). Due to small node size and limited solution capability of the currently available Quantum computers and/or simulators, we develop a hybrid approach where subgraphs of a TSP tour are executed on a Quantum computer and the results from the quantum optimization are combined in further optimization of the whole tour. Since the local optimization of a subgraph is prone to getting stuck in a local minima, we overcome this problem by using a parallel ant colony optimization algorithm with periodic pheromone exchange between colonies. Our results are encouraging and yield optimum results for benchmarks with less than 50 nodes, and usually within 1% of the optimal solution for benchmarks with around 200 nodes.

关键词： quantum algorithm quantum approximate optimization algorithm combinatorial optimization problem ant colony optimization algorithm

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An Adaptive ant colony optimization in Knowledge Graphs 11

An Adaptive Ant Colony Optimization in Knowledge Graphs

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11th IEEE International Conference on Knowledge Graph (IEEE ICKG)

作者： Li, Wei Xia, Le Huang, Ying Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou Peoples R China Gannan Normal Univ Inst Math & Comp Sci Ganzhou Peoples R China

ISBN: (纸本)9781728181561

Knowledge graphs have been widely used in various fields such as question answering systems and recommendation systems. However, there are few researchers on combinatorial optimization problems based on knowledge graphs, which greatly delays the development of knowledge graphs. Also, when solving combinatorial optimization problems only by using knowledge graphs, it is impossible to obtain better results. In order to solve these problems, an ant colony optimization algorithm based on an adaptive strategy (AACO) is proposed, and the algorithm is applied to solve the path optimization model established by the knowledge graph. In the vector space based on knowledge graph embedding, the ant colony optimization algorithm has a good positive feedback mechanism and robustness to find effective paths between entity nodes. Experimental results show that this proposed AACO algorithm can accelerate the convergence speed and obtain better accuracy. At the same time, a global optimal solution can be achieved, which is suitable for solving combinatorial optimization problems.

关键词： knowledge graph ant colony optimization algorithm adaptive strategy combinatorial optimization problems

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An Approach to Feature Selection Based on ant colony optimization and Rough Set

An Approach to Feature Selection Based on Ant Colony Optimiz...

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International Conference on Intelligent Computing and Information Science

作者： Wu, Junyun Qiu, Taorong Wang, Lu Huang, Haiquan Nanchang Univ Dept Comp Nanchang 330031 Jiangxi Peoples R China

ISBN: (纸本)9783642181283

Feature selection plays an important role in many fields. This paper proposes a method for feature selection which combined the rough set method and ant colony optimization algorithm. The algorithm used the attribute dependence and the attribute importance as the inspiration factor which applied to the transfer rules. For further, the quality of classification based on rough set method and the length of the feature subset were used to build the pheromone update strategy. Through the test of data set, results show that the proposed method is feasible.

关键词： Feature selection ant colony optimization algorithm Rough set

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VS-TACO: A Tuned Version of ant colony optimization for Generating Variable Strength Interaction in T-Way Testing Strategy 2022

VS-TACO: A Tuned Version of Ant Colony Optimization for Gene...

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11th International Conference on Software and Computer Applications, ICSCA 2022

作者： Zahir Ahmad, Mohd Zamri Othman, Rozmie Razif Ramli, Nuraminah Rashid Ali, Mohd Shaiful Aziz Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Arau Perlis02600 Malaysia

ISBN: (纸本)9781450385770

Ever since, software technologies have been through a rapid evolution. In a real application, the interaction between input variables may vary, thus the exhaustive testing is no longer practical since it is time-consuming and lead to combinatorial explosion. One of the strategies that able to cater fault due to the interaction is ant colony optimization (ACO) algorithm. Typically, amount of ants in the ACO algorithm is fixed at certain number while the search space technique (i.e. to explore or exploit new possible solutions) is randomized for each iteration in the entire algorithm, are potentially affect the optimization's efficiency. Thus this paper proposes a new variant of ACO algorithm called as a tuned version of ACO for generating variable strength interaction in t-way testing strategy (VS-TACO). VS-TACO applied a Mamdani fuzzy logic in order to dynamically choose the number of ant and decide which search space technique to be used. Experiments that have been conducted on VS-TACO and benchmarked with other strategies, shows VS-TACO produce a competitive result in term of test suite size. © 2022 ACM.

关键词： t-way testing ant colony optimization algorithm variable strength interaction testing

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ant colony Based on Cat Swarm optimization and Application in Picking Robot Path Planning 7

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

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7th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Xun, Yanqin Liu, Mingshan Zhou, Yuan Wang, Rui Zhang, Wenbo Jilin Univ Coll Commun Engn Changchun Jilin Province Peoples R China

ISBN: (纸本)9781467399043

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 optimum. To solve these problems, the ACO based on cat swarm optimization (CSO) algorithm searching model (CSOACO) is presented. In 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 algorithm. Finally, the new algorithm is simulated in MATLAB for picking robot multi-objective path planning problem. Through the simulation analysis, not only set the parameters, but compare CSOACO with other algorithms. Simulation results show that the algorithm can accelerate the convergence speed, search to the global optimal solution and realize the multi-objective path planning of picking robot.

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

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