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    Wyświetlanie 1-9 z 9
    Tytuł:
    Modelling of ship’s trajectory planning in collision situations by hybrid genetic algorithm
    Autorzy:
    Ni, S.
    Liu, Z.
    Cai, Y.
    Wang, X.
    Powiązania:
    https://bibliotekanauki.pl/articles/259640.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    trajectory planning
    Multiple Genetic Algorithm
    ship collision avoidance
    nonlinear programming
    COLREGs
    Opis:
    Ship collision-avoidance trajectory planning aims at searching for a theoretical safe-critical trajectory in accordance with COLREGs and good seamanship. In this paper, a novel optimal trajectory planning based on hybrid genetic algorithm is presented for ship collision avoidance in the open sea. The proposed formulation is established based on the theory of the Multiple Genetic Algorithm (MPGA) and Nonlinear Programming, which not only overcomes the inherent deficiency of the Genetic Algorithm (GA) for premature convergence, but also guarantees the practicality and consistency of the optimal trajectory. Meanwhile, the encounter type as well as the obligation of collision avoidance is determined according to COLREGs, which is then considered as the restricted condition for the operation of population initialization. Finally, this trajectory planning model is evaluated with a set of test cases simulating various traffic scenarios to demonstrate the feasibility and superiority of the optimal trajectory.
    Źródło:
    Polish Maritime Research; 2018, 3; 14-25
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Correlated mutations in selected protein families
    Autorzy:
    Leluk, J.
    Sobczyk, M.
    Becella, Ł.
    Powiązania:
    https://bibliotekanauki.pl/articles/1986920.pdf
    Data publikacji:
    2002
    Wydawca:
    Politechnika Gdańska
    Tematy:
    protein sequence
    multiple alignment
    tertiary structure
    mutational correlation
    genetic semihomology algorithm
    Opis:
    Four different protein families (two proteinase inhibitor families, myoglobins and lysozymes) were surveyed for correlated mutations with respect to the position distance and their significance in structure stabilization and biological activity. They were chosen for this study in order to verify the currently admitted model of mutational correlation relationship with respect to spatial contact of the residues and contribution in protein biological activity. There was observed high contribution of spatially dispersed residues (which are also not involved in the protein active center) in mutational correlation. Because of the significantly large distance between correlated positions these cases do not correspond explicitly to any mechanism included in current hypotheses. It is suggested that the role of residue spatial contact in structure preservation, intermolecular interaction and active site rescue mechanisms only partially explains the correlation phenomenon.
    Źródło:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2002, 6, 3; 469-482
    1428-6394
    Pojawia się w:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
    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
    Tytuł:
    Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming
    Autorzy:
    Arif, Salah
    Djellal, Adel
    Djebbari, Nawel
    Belhaoues, Saber
    Touati, Hassen
    Guellati, Fatma Zohra
    Bensouilah, Mourad
    Powiązania:
    https://bibliotekanauki.pl/articles/2174666.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    microcystis cell density
    Multiple Linear Regression
    Support Vector Machine
    Particle Swarm Optimization
    Genetic Algorithm
    Bird Swarm Algorithm
    Opis:
    Caused by excess levels of nutrients and increased temperatures, freshwater cyanobacterial blooms have become a serious global issue. However, with the development of artificial intelligence and extreme learning machine methods, the forecasting of cyanobacteria blooms has become more feasible. We explored the use of multiple techniques, including both statistical [Multiple Regression Model (MLR) and Support Vector Machine (SVM)] and evolutionary [Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bird Swarm Algorithm (BSA)], to approximate models for the prediction of Microcystis density. The data set was collected from Oubeira Lake, a natural shallow Mediterranean lake in the northeast of Algeria. From the correlation analysis of ten water variables monitored, six potential factors including temperature, ammonium, nitrate, and ortho-phosphate were selected. The performance indices showed; MLR and PSO provided the best results. PSO gave the best fitness but all techniques performed well. BSA had better fitness but was very slow across generations. PSO was faster than the other techniques and at generation 20 it passed BSA. GA passed BSA a little further, at generation 50. The major contributions of our work not only focus on the modelling process itself, but also take into consideration the main factors affecting Microcystis blooms, by incorporating them in all applied models.
    Źródło:
    Geomatics and Environmental Engineering; 2023, 17, 2; 31--68
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Synthesis of Reconfigurable Multiple Shaped Beams of a Concentric Circular Ring Array Antenna Using Evolutionary Algorithms
    Autorzy:
    Dubey, Sanjay Kumar
    Mandal, Debasis
    Powiązania:
    https://bibliotekanauki.pl/articles/2200965.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    cosec2 beam
    differential evolution algorithm (DE)
    firefly algorithm (FA)
    flat top beam
    genetic algorithm (GA)
    multiple shaped beam patterns
    pencil beam
    Opis:
    The approach described in this paper uses evolutionary algorithms to create multiple-beam patterns for a concentric circular ring array (CCRA) of isotropic antennas using a common set of array excitation amplitudes. The flat top, cosec2, and pencil beam patterns are examples of multiple-beam patterns. All of these designs have an upward angle of θ = 0◦. All the patterns are further created in three azimuth planes (φ = 0◦, 5◦, and 10◦). To create the necessary patterns, non-uniform excitations are used in combination with evenly spaced isotropic components. For the flat top and cosecant-squared patterns, the best combination of common components, amplitude and various phases is applied, whereas the pencil beam pattern is produced using the common amplitude only. Differential evolutionary algorithm (DE), genetic algorithm (GA), and firefly algorithm (FA) are used to generate the best 4-bit discrete magnitudes and 5-bit discrete phases. These discrete excitations aid in lowering the feed network design complexity and the dynamic range ratio (DRR). A variety of randomly selected azimuth planes are used to verify the excitations as well. With small modifications in the desired parameters, the patterns are formed using the same excitation. The results proved both the efficacy of the suggested strategy and the dominance of DE over GA as well as FA.
    Źródło:
    Journal of Telecommunications and Information Technology; 2023, 1; 8--17
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modeling Pollution Index Using Artificial Neural Network and Multiple Linear Regression Coupled with Genetic Algorithm
    Autorzy:
    Abdulkareem, Iman Ali
    Abbas, Abdulhussain A.
    Dawood, Ammar Salman
    Powiązania:
    https://bibliotekanauki.pl/articles/2068477.pdf
    Data publikacji:
    2022
    Wydawca:
    Polskie Towarzystwo Inżynierii Ekologicznej
    Tematy:
    Shatt Al-Arab river
    comprehensive pollution index
    multiple linear regression
    artificial neural network
    genetic algorithm
    Opis:
    Shatt Al-Arab River in Basrah province, Iraq, was assessed by applying comprehensive pollution index (CPI) at fifteen sampling locations from 2011 to 2020, taking into consideration twelve physicochemical parameters which included pH, Tur., TDS, EC, TH, Na+, K+, Ca+2, Mg+2, Alk., SO4-2, and Cl-. The effectiveness of multiple linear regression (MLR) and artificial neural network (ANN) for predicting comprehensive pollution index was examined in this research. In order to determine the ideal values of the predictor parameters that lead to the lowest CPI value, the genetic algorithm coupled with multiple linear regression (GA-MLR) was used. A multi-layer feed-forward neural network with backpropagation algorithm was used in this study. The optimal ANN structure utilized in this research consisted of three layers: the input layer, one hidden layer, and one output layer. The predicted equation of the comprehensive pollution index was created using the regression technique and used as an objective function of the genetic algorithm. The minimum predicted comprehensive pollution index value recommended by the GA-MLR approach was 0.3777.
    Źródło:
    Journal of Ecological Engineering; 2022, 23, 3; 236--250
    2299-8993
    Pojawia się w:
    Journal of Ecological Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The On-line Evolutionary Method for Soft Fault Diagnosis in Diode-transistor Circuits
    Autorzy:
    Korzybski, M.
    Ossowski, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/226980.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    electric circuit diagnosis
    soft faults
    multiple faults
    evolutionary computation
    gene expression programming
    genetic algorithm
    differential evolution
    Opis:
    The paper is devoted to diagnostic method enabling us to perform all the three levels of fault investigations - detection, localization and identification. It is designed for analog diode-transistor circuits, in which the circuit’s state is defined by the DC sources’ values causing elements operating points and the harmonic components with small amplitudes being calculated in accordance with small-signal circuit analysis rules. Geneexpression programming (GEP), differential evolution (DE) and genetic algorithms (GA) are a mathematical background of the proposed algorithms. Time consumed by diagnostic process rises rapidly with the increasing number of possible faulty circuit elements in case of using any of mentioned algorithms. The conncept of using two different circuit models with partly different elements allows us to decrease a number of possibly faulty elements in each circuit because some of possibly faulty elements are absent in one of two investigated circuits.
    Źródło:
    International Journal of Electronics and Telecommunications; 2015, 61, 1; 109-115
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Cost-sensitive classifier ensemble for medical decision support system
    Autorzy:
    Woźniak, M.
    Zmyślony, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/333365.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    klasyfikacja wrażliwa cenowo
    systemy wielo-klasyfikatora
    rozpoznawanie obrazów
    algorytm genetyczny
    cost-sensitive classification
    multiple classifier systems
    pattern recognition
    genetic algorithm
    Opis:
    Multiple classifier systems are currently the focus of intense research. In this conceptual approach, the main effort focuses on establishing decision on the basis of a set of individual classifiers' outputs. This approach is well known but usually most of propositions do not take exploitation cost of such a classifier under consideration. The paper deals with the problem how to take a test acquisition cost during classification task under the framework of combined approach on board. The problem is known as cost-sensitive classification and it has been usually considered for the decision tree induction. In this work we adapt mentioned above idea into choosing members of classifier ensemble and propose a method of choosing a pool of individual classifiers which take into consideration on the one hand quality of ensemble on the other hand cost of classification. Properties of mentioned concept are established during computer experiments conducted on chosen medical benchmark databases from UCI Machine Learning Repository.
    Źródło:
    Journal of Medical Informatics & Technologies; 2011, 17; 97-104
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Control of perishable inventory system with uncertain perishability process using neural networks and robust multicriteria optimization
    Autorzy:
    Chołodowicz, Ewelina
    Orłowski, Przemysław
    Powiązania:
    https://bibliotekanauki.pl/articles/2173677.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    multiple objective programming
    optimal control
    genetic algorithm
    perishable inventories
    inventory control
    programowanie wielokryterialne
    optymalna kontrola
    algorytm genetyczny
    produkt szybko psujący się
    kontrola zapasów
    Opis:
    The inventory systems are highly variable and uncertain due to market demand instability, increased environmental impact, and perishability processes. The reduction of waste and minimization of holding and shortage costs are the main topics studied within the inventory management area. The main difficulty is the variability of perishability and other processes that occurred in inventory systems and the solution for a trade-off between sufficient inventory level and waste of products. In this paper, the approach for resolving this trade-off is proposed. The presented approach assumes the application of a state-feedback neural network controller to generate the optimal quantity of orders considering an uncertain deterioration process and the FIFO issuing policy. The development of the control system is based on state-space close loop control along with neural networks. For modelling the perishability process Weibull distribution and FIFO policy are applied. For the optimization of the designed control system, the evolutionary NSGA-II algorithm is used. The robustness of the proposed approach is provided using the minimax decision rule. The worst-case scenario of an uncertain perishability process is considered. For assessing the proposed approach, simulation research is conducted for different variants of controller structure and model parameters. We perform extensive numerical simulations in which the assessment process of obtained solutions is conducted using hyper volume indicator and average absolute deviation between results obtained for the learning and testing set. The results indicate that the proposed approach can significantly improve the performance of the perishable inventory system and provides robustness for the uncertain changes in the perishability process.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e141182
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-9 z 9

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