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    Wyświetlanie 1-5 z 5
    Tytuł:
    Performance investigation and element optimization of 2D array transducer using Bat Algorithm
    Autorzy:
    Tantawy, Dina Mohamed
    Eladawy, Mohamed
    Hassan, Mohamed Alimaher
    Mubarak, Roaa
    Powiązania:
    https://bibliotekanauki.pl/articles/140685.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    2D ultrasound arrays
    Binary Bat Algorithm
    Genetic Algorithm
    Optimization
    Opis:
    One of the least expensive and safest diagnostic modalities routinely used is ultrasound imaging. An attractive development in this field is a two-dimensional (2D) matrix probe with three-dimensional (3D) imaging. The main problems to implement this probe come from a large number of elements they need to use. When the number of elements is reduced the side lobes arising from the transducer change along with the grating lobes that are linked to the periodic disposition of the elements. The grating lobes are reduced by placing the elements without any consideration of the grid. In this study, the Binary Bat Algorithm (BBA) is used to optimize the number of active elements in order to lower the side lobe level. The results are compared to other optimization methods to validate the proposed algorithm.
    Źródło:
    Archives of Electrical Engineering; 2020, 69, 3; 561-579
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm
    Autorzy:
    Dziwiński, Piotr
    Bartczuk, Łukasz
    Paszkowski, Józef
    Powiązania:
    https://bibliotekanauki.pl/articles/1837533.pdf
    Data publikacji:
    2020
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    hybrid methods
    Particle Swarm Optimization
    Genetic Algorithm
    fuzzy systems
    multimodal function
    Opis:
    The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 2; 95-111
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    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:
    Dynamics of Stochastic vs. Greedy Heuristics in Traveling Salesman Problem
    Autorzy:
    Białogłowski, M.
    Staniaszek, M.
    Laskowski, W.
    Grudniak, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/91276.pdf
    Data publikacji:
    2018
    Wydawca:
    Warszawska Wyższa Szkoła Informatyki
    Tematy:
    traveling salesman problem
    Nearest Neighbor
    Monte Carlo
    Simulated Annealing
    Genetic Algorithm
    particle swarm optimization (PSO)
    Opis:
    We studied the relative performance of stochastic heuristics in order to establish the relations between the fundamental elements of their mechanisms. The insights on their dynamics, abstracted from the implementation details, may contribute to the development of an efficient framework for design of new probabilistic methods. For that, we applied four general optimization heuristics with varying number of hyperparameters to traveling salesman problem. A problem-specific greedy approach (Nearest Neighbor) served as a reference for the results of: Monte Carlo, Simulated Annealing, Genetic Algorithm, and Particle Swarm Optimization. The more robust heuristics – with higher configuration potential, i.e. with more hyperparameters – outperformed the smart ones, being surpassed only by the method specifically designed for the task.
    Źródło:
    Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2018, 12, 19; 7-24
    1896-396X
    2082-8349
    Pojawia się w:
    Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Global path planning for multiple AUVs using GWO
    Autorzy:
    Panda, Madhusmita
    Das, Bikramaditya
    Pati, Bibhuti
    Powiązania:
    https://bibliotekanauki.pl/articles/229749.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Autonomous Underwater Vehicle
    AUV
    Genetic Algorithm
    GA
    Global Path Planning
    GPP
    Grey Wolf Optimization
    GWO
    Sliding Mode Control
    SMC
    waypoints
    Opis:
    In global path planning (GPP), an autonomous underwater vehicle (AUV) tracks a predefined path. The main objective of GPP is to generate a collision free sub-optimal path with minimum path cost. The path is defined as a set of segments, passing through selected nodes known as waypoints. For smooth planar motion, the path cost is a function of the path length, the threat cost and the cost of diving. Path length is the total distance travelled from start to end point, threat cost is the penalty of collision with the obstacle and cost of diving is the energy expanse for diving deeper in ocean. This paper addresses the GPP problem for multiple AUVs in formation. Here, Grey Wolf Optimization (GWO) algorithm is used to find the suboptimal path for multiple AUVs in formation. The results obtained are compared to the results of applying Genetic Algorithm (GA) to the same problem. GA concept is simple to understand, easy to implement and supports multi-objective optimization. It is robust to local minima and have wide applications in various fields of science, engineering and commerce. Hence, GA is used for this comparative study. The performance analysis is based on computational time, length of the path generated and the total path cost. The resultant path obtained using GWO is found to be better than GA in terms of path cost and processing time. Thus, GWO is used as the GPP algorithm for three AUVs in formation. The formation follows leader-follower topography. A sliding mode controller (SMC) is developed to minimize the tracking error based on local information while maintaining formation, as mild communication exists. The stability of the sliding surface is verified by Lyapunov stability analysis. With proper path planning, the path cost can be minimized as AUVs can reach their target in less time with less energy expanses. Thus, lower path cost leads to less expensive underwater missions.
    Źródło:
    Archives of Control Sciences; 2020, 30, 1; 77-100
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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