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    Tytuł:
    Verification of ships trajectory planning algorithms using real navigational data
    Autorzy:
    Lazarowska, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/117103.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    Visibility Graph-search Algorithm (VGA)
    Discrete Artificial Potential Field (DAPF)
    real navigational data
    ARPA
    ship's trajectory planning
    ship's trajectory planning algorithms
    Ant Colony Optimization (ACO)
    Trajectory Base Algorithm (TBA)
    Opis:
    The paper presents results of ship's safe trajectory planning algorithms verification. Real navigational data registered from a radar with an Automatic Radar Plotting Aid on board the research and training ship Horyzont II were used as input data to the algorithms. The algorithms verified in the presented research include the Ant Colony Optimization algorithm (ACO), the Trajectory Base Algorithm (TBA), the Visibility Graph-search Algorithm (VGA) ant the Discrete Artificial Potential Field algorithm (DAPF). Details concerning data registration and exemplary results obtained with the use or real navigational data are introduced and summarized in the paper. Presented results prove the applicability of proposed algorithms for solving the ship's safe trajectory planning problem.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2019, 13, 3; 559-564
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multi-criteria ACO-based algorithm for ship’s trajectory planning
    Autorzy:
    Lazarowska, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/116548.pdf
    Data publikacji:
    2017
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    colregs
    ships manoeuvering
    Ant Colony Optimization (ACO)
    ship’s trajectory planning
    route planning
    multi-criteria ACO-based algorithm
    guidance
    navigation and control (GNC)
    ARPA
    Opis:
    The paper presents a new approach for solving a path planning problem for ships in the environment with static and dynamic obstacles. The algorithm utilizes a heuristic method, classified to the group of Swarm Intelligence approaches, called the Ant Colony Optimization. The method is inspired by a collective behaviour of ant colonies. A group of agents - artificial ants searches through the solution space in order to find a safe, optimal trajectory for a ship. The problem is considered as a multi-criteria optimization task. The criteria taken into account during problem solving are: path safety, path length, the International Regulations for Preventing Collisions at Sea (COLREGs) compliance and path smoothness. The paper includes the description of the new multi-criteria ACO-based algorithm along with the presentation and discussion of simulation tests results.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2017, 11, 1; 31-36
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An improved ant colony optimization algorithm and its application to text-independent speaker verification system
    Autorzy:
    Aghdam, M. H.
    Powiązania:
    https://bibliotekanauki.pl/articles/91678.pdf
    Data publikacji:
    2012
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    ant colony
    optimization
    ant colony optimization
    ACO
    security
    automatic speaker verification
    ASV
    feature space
    Gaussian mixture model universal background model
    GMM-UBM
    Opis:
    With the growing trend toward remote security verification procedures for telephone banking, biometric security measures and similar applications, automatic speaker verification (ASV) has received a lot of attention in recent years. The complexity of ASV system and its verification time depends on the number of feature vectors, their dimensionality, the complexity of the speaker models and the number of speakers. In this paper, we concentrate on optimizing dimensionality of feature space by selecting relevant features. At present there are several methods for feature selection in ASV systems. To improve performance of ASV system we present another method that is based on ant colony optimization (ACO) algorithm. After feature selection phase, feature vectors are applied to a Gaussian mixture model universal background model (GMM-UBM) which is a text-independent speaker verification model. The performance of proposed algorithm is compared to the performance of genetic algorithm on the task of feature selection in TIMIT corpora. The results of experiments indicate that with the optimized feature set, the performance of the ASV system is improved. Moreover, the speed of verification is significantly increased since by use of ACO, number of features is reduced over 80% which consequently decrease the complexity of our ASV system.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 4; 301-315
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An ACO Path Planner Using a FIS for Path Selection Adjusted with a Simple Tuning Algorithm
    Autorzy:
    Porta-Garcia, M.
    Montiel, O.
    Sepulveda, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/384490.pdf
    Data publikacji:
    2008
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    ant colony optimization
    ACO
    autonomous mobile robot
    navigation
    fuzzy logic
    path planning
    Opis:
    This paper presents a path planner application for mobile robots based on Ant Colony Optimization (ACO). The selection of the optimal path relies in the criterion of a Fuzzy Inference System (FIS), which is adjusted using a Simple Tuning Algorithm (STA). The path planner can be executed in Mode I and Mode II. The first mode only works in the virtual environment of the interface, while Mode II embraces the wireless communication with a real robot; once the ACO algorithm finds the best route, the coordinates are sent to a mobile robot via Bluetooth communication; if the robot senses a new obstacle, the computer is notified and does a rerouting routine in order to avoid the obstacle and reach the goal. In other words, the application supports dynamic search spaces.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 1; 5-11
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid ant colony for multiresponse mixed-integer problems
    Autorzy:
    Kushwaha, S.
    Mukherjee, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/409419.pdf
    Data publikacji:
    2012
    Wydawca:
    Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
    Tematy:
    ant colony optimization(ACO)
    desirability functions
    genetic algorithm (GA)
    multiple response optimization(MRO)
    Opis:
    In this paper, a hybrid ant colony optimization (ACO) is used to solve a multiple response optimization problem with mixed-integer (MI) search space. The work reported in this paper may be clasified into three part. The first part discusses on relevant litratures and the methodology to solve multiple response optimization problem. The second part provide details on the working principal, parameter tuning of a hybrid ACO proposed for mixed integer state space. In the hybrid ACO, genetic algorithm (GA) is used for intensification of the search strategy. Standard single response (objective) test functions are selected to verify the suitability of hybrid ACO. The third part of this research work illustrates the application of the hybrid ACO in a multiple response optimization (MRO) problem. Statistical experimentation, partial least square regression, 'maximin' desirability function, and hybrid ACO is used to solve the MRO problem. The results confirm the suitability of the hybid ACO for a typical MI MRO problem.
    Źródło:
    Research in Logistics & Production; 2012, 2, 4; 317-327
    2083-4942
    2083-4950
    Pojawia się w:
    Research in Logistics & Production
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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