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    Wyświetlanie 1-6 z 6
    Tytuł:
    Function optimization using metaheuristics
    Autorzy:
    Pilski, M.
    Seredyński, F.
    Powiązania:
    https://bibliotekanauki.pl/articles/92887.pdf
    Data publikacji:
    2006
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    particle swarm optimization (PSO)
    artificial immune system
    genetic algorithm
    function optimization
    Opis:
    The paper presents the results of comparison of three metaheuristics that currently exist in the problem of function optimization. The first algorithm is Particle Swarm Optimization (PSO) - the algorithm has recently emerged. The next one is based on a paradigm of Artificial Immune System (AIS). Both algorithms are compared with Genetic Algorithm (GA). The algorithms are applied to optimize a set of functions well known in the area of evolutionary computation. Experimental results show that it is difficult to unambiguously select one best algorithm which outperforms other tested metaheuristics.
    Źródło:
    Studia Informatica : systems and information technology; 2006, 1(7); 77-91
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimization of electric and magnetic field intensities in proximity of power lines using genetic and particle swarm algorithms
    Autorzy:
    Król, K.
    Machczyński, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/141588.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    power line
    electric field
    magnetic field
    optimization
    genetic algorithm
    particle swarm algorithm
    Opis:
    The paper presents optimization of power line geometrical parameters aimed to reduce the intensity of the electric field and magnetic field intensity under an overhead power line with the use of a genetic algorithm (AG) and particle swarm optimization (PSO). The variation of charge distribution along the conductors as well as the sag of the overhead line and induced currents in earth wires were taken into account. The conductor sag was approximated by a chain curve. The charge simulation method (CSM) and the method of images were used in the simulations of an electric field, while a magnetic field were calculated using the Biot–Savart law. Sample calculations in a three-dimensional system were made for a 220 kV single – circuit power line. A comparison of the used optimization algorithms was made.
    Źródło:
    Archives of Electrical Engineering; 2018, 67, 4; 829-843
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparative Study of Optimised Artificial Intelligence Based First Order Sliding Mode Controllers for Position Control of a DC Motor Actuator
    Autorzy:
    Nyong-Bassey, B. E.
    Akinloye, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/385114.pdf
    Data publikacji:
    2016
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    adaptive fuzzy control
    DC motor position control
    genetic algorithm
    particle swarm optimization (PSO)
    sliding mode control
    Opis:
    This paper aims at critically reviewing various sliding mode control measures applied to Permanent Magnet DC Motor actuator for position control. At first, a hybrid sliding mode controller was examined with its advantages and disadvantages. Then, the smooth sliding mode controller in the same manner. The shortcomings of the two methods were overcome by proper switch design and also using tanh-sinh hyperbolic function. The sliding mode controller switches on when either disturbance or noise is detected. Genetic Algorithm Computational tuning technique is employed to optimize the gains of the controllers for optimal response.The performance of the proposed controller architecture, as well as the reviewed controllers, have been compared for performance evaluation with respect to several operating conditions. This includes load torque disturbance injection, noise injection in a feedback loop, motor nonlinearity exhibited by parameters variation, and a step change in reference input demand.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2016, 10, 3; 58-71
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An adaptive particle swarm optimization algorithm for robust trajectory tracking of a class of under actuated system
    Autorzy:
    Kumar, V. E.
    Jerome, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/141105.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    inverted pendulum
    LQR controller
    particle swarm optimization (PSO)
    genetic algorithm
    adaptive inertia weight factor
    state feedback control
    Opis:
    This paper presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial and error approach to obtain the optimum controller gains, but it is often cumbersome and tedious to tune the controller gains via trial and error method. To address this problem, an intelligent approach employing adaptive PSO (APSO) for optimum tuning of LQR is proposed. In this approach, an adaptive inertia weight factor (AIWF), which adjusts the inertia weight according to the success rate of the particles, is employed to not only speed up the search process but also to increase the accuracy of the algorithm towards obtaining the optimum controller gain. The performance of the proposed approach is tested on a bench mark inverted pendulum system, and the experimental results of APSO are compared with that of the conventional PSO and GA. Experimental results prove that the proposed algorithm remarkably improves the convergence speed and precision of PSO in obtaining the robust trajectory tracking of inverted pendulum.
    Źródło:
    Archives of Electrical Engineering; 2014, 63, 3; 345-365
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Cross‐Comparison of Evolutionary Algorithms for Optimizing Design of Sustainable Supply Chain Network under Disruption Risks
    Autorzy:
    Al-Zuheri, Atiya
    Powiązania:
    https://bibliotekanauki.pl/articles/2023790.pdf
    Data publikacji:
    2021
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    comparison
    genetic algorithm
    particle swarm optimization
    sustainable supply chain design
    disruption risk
    porównanie
    algorytm genetyczny
    optymalizacja rojem cząstek
    projektowanie zrównoważonego łańcucha dostaw
    ryzyko zakłóceń
    Opis:
    Optimization of a sustainable supply chain network design (SSCND) is a complex decision-making process which can be done by the optimal determination of a set of decisions and constraints such as the selection of suppliers, transportation-related facilities and distribution centres. Different optimization techniques have been applied to handle various SSCND problems. Meta- heuristic algorithms are developed from these techniques that are commonly used to solving supply chain related problems. Among them, Genetic algorithms (GA) and particle swarm optimization (PSO) are implemented as optimization solvers to obtain supply network design decisions. This paper aims to compare the performance of these two evolutionary algorithms in optimizing such problems by minimizing the total cost that the system faces to potential disruption risks. The mechanism and implementation of these two evolutionary algorithms is presented in this paper. Also, using an optimization considers ordering, purchasing, inventory, transportation, and carbon tax cost, a numerical real-life case study is presented to demonstrate the validity of the effectiveness of these algorithms. A comparative study for the algorithms performance has been carried out based on the quality of the obtained solution and the results indicate that the GA performs better than PSO in finding lower-cost solution to the addressed SSCND problem. Despite a lot of research literature being done regarding these two algorithms in solving problems of SCND, few studies have compared the optimization performance between GA and PSO, especially the design of sustainable systems under risk disruptions.
    Źródło:
    Advances in Science and Technology. Research Journal; 2021, 15, 4; 342-351
    2299-8624
    Pojawia się w:
    Advances in Science and Technology. Research Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An Analytical Study for the Role of Fuzzy Logic in Improving Metaheuristic Optimization Algorithms
    Autorzy:
    Vij, Sonakshi
    Jain, Amita
    Tayal, Devendra
    Castillo, Oscar
    Powiązania:
    https://bibliotekanauki.pl/articles/385121.pdf
    Data publikacji:
    2018
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    fuzzy logic
    metaheuristics
    evolutionary computing
    genetic algorithm
    particle swarm optimization (PSO)
    ant colony optimization
    fuzzy evolutionary algorithm
    fuzzy cuckoo
    fuzzy simulated annealing
    fuzzy swarm intelligence
    fuzzy differential evolution
    tabu
    fuzzy mutation
    fuzzy natural selection
    fuzzy fitness function
    big bang big crunch
    fuzzy bacterial
    neuro fuzzy logic
    logika rozmyta
    metaheurystyka
    obliczenia ewolucyjne
    algorytm genetyczny
    optymalizacja roju cząstek
    optymalizacja kolonii mrówek
    Opis:
    The research applications of fuzzy logic have always been multidisciplinary in nature due to its ability in handling vagueness and imprecision. This paper presents an analytical study in the role of fuzzy logic in the area of metaheuristics using Web of Science (WoS) as the data source. In this case, 178 research papers are extracted from it in the time span of 1989-2016. This paper analyzes various aspects of a research publication in a scientometric manner. The top cited research papers, country wise contribution, topmost organizations, top research areas, top source titles, control terms and WoS categories are analyzed. Also, the top 3 fuzzy evolutionary algorithms are extracted and their top research papers are mentioned along with their topmost research domain. Since neuro fuzzy logic poses feasible options for solving numerous research problems, hence a section is also included by the authors to present an analytical study regarding research in it. Overall, this study helps in evaluating the recent research patterns in the field of fuzzy metaheuristics along with envisioning the future trends for the same. While on one hand this helps in providing a new path to the researchers who are beginners in this field as they can start exploring it through the analysis mentioned here, on the other hand it provides an insight to professional researchers too who can dig a little deeper in this field using knowledge from this study.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2018, 12, 4; 11-27
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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