Wyniki wyszukiwania - Biblioteka Nauki

1

Particle swarm methods for transport and logistics optimization processes

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Lisowski J.

Journal of Automation, Electronics and Electrical Engineering

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2023

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tom Vol. 5, No. 1

21--34

EN

This article describes heuristic approaches to static optimization methods, taking into account the nature of the motion of a swarm of particles representing, for example, birds, ants, bees, fireflies, bats, krill, cuckoos, cuttlefish, cockroaches, or the pollination process of flowers. In the given descriptions of the methods, the features of swarm intelligence are detailed, the optimization quality indicators are formulated, and flow charts of the computational algorithms are provided.

PL

W artykule przedstawiono heurystyczne metody optymalizacji statycznej wykorzystujące naturalne zasady ruchu roju cząstek - ptaków, mrówek, pszczół, świetlików, nietoperzy, kryli, kukułek, mątw, karaluchów i zapylania kwiatów. Dla każdej z metod opisano cechę inteligencji roju, przedstawiono postać funkcji celu oraz podano zasadę działania algorytmu optymalizacji.

2

Multi-criteria optimisation of multi-stage positional game of vessels

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Lisowski J.

Polish Maritime Research

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2020

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tom nr 1

46--52

EN

The paper presents a mathematical model of a positional game of the safe control of a vessel in collision situations at sea, containing a description of control, state variables and state constraints as well as sets of acceptable ship strategies, as a multi-criteria optimisation task. The three possible tasks of multi-criteria optimisation were formulated in the form of non-cooperative and cooperative multi-stage positional games as well as optimal non-game controls. The multicriteria control algorithms corresponding to these tasks were subjected to computer simulation in Matlab/Simulink software based on the example of the real navigational situation of the passing of one’s own vessel with eighteen objects encountered in the North Sea.

3

Multi-criteria multi-stage game optimization

100%

Lisowski J.

Journal of Automation, Electronics and Electrical Engineering

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2022

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tom Vol. 4, No. 1

37--42

EN

The article presents a mathematical model of a multi-stage game of the process of safe control of a transport object in possible collision situations with other encountered objects, containing a description of state variables, state and control constraints, and sets of permissible object strategies. Multi-criteria optimization tasks were formulated in the form of positional and matrix games under the conditions of playing non-cooperative and cooperative control as well as non-game optimal control. The multi-criteria control algorithms corresponding to these tasks were computer simulated in Matlab / Simulink on the example of a real situation.

PL

W artykule przedstawiono model matematyczny wieloetapowej gry procesu bezpiecznego sterowania obiektem transportowym w możliwych sytuacjach kolizyjnych z innymi spotkanymi obiektami, zawierający opis zmiennych stanu, ograniczeń stanu i sterowania oraz zbiory dopuszczalnych strategii obiektów. Sformułowano wielokryterialne zadania optymalizacyjne w postaci gry pozycyjnej i macierzowej, w warunkach rozgrywającego sterowania niekooperacyjnego i kooperacyjnego oraz nierozgrywającego sterowania optymalnego. Algorytmy sterowania wielokryterialnego odpowiadające tym zadaniom poddano symulacji komputerowej w programie Matlab/Simulink na przykładzie rzeczywistej sytuacji.

4

Computational intelligence in marine control engineering education

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Lisowski J.

Polish Maritime Research

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2021

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tom nr 1

163--172

EN

This paper presents a new approach to the existing training of marine control engineering professionals using artificial intelligence. We use optimisation strategies, neural networks and game theory to support optimal, safe ship control by applying the latest scientific achievements to the current process of educating students as future marine officers. Recent advancements in shipbuilding, equipment for robotised ships, the high quality of shipboard game plans, the cost of overhauling, dependability, the fixing of the shipboard equipment and the requesting of the safe shipping and environmental protection, requires constant information on recent equipment and programming for computational intelligence by marine officers. We carry out an analysis to determine which methods of artificial intelligence can allow us to eliminate human subjectivity and uncertainty from real navigational situations involving manoeuvring decisions made by marine officers. Trainees learn by using computer simulation methods to calculate the optimal safe traverse of the ship in the event of a possible collision with other ships, which are mapped using neural networks that take into consideration the subjectivity of the navigator. The game-optimal safe trajectory for the ship also considers the uncertainty in the navigational situation, which is measured in terms of the risk of collision. The use of artificial intelligence methods in the final stage of training on ship automation can improve the practical education of marine officers and allow for safer and more effective ship operation.

5

Sensitivity of transport object control quality for measuring the inaccuracy of state variables

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Lisowski J.

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2024

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tom Vol. 6, No. 1

19--30

EN

This work analyzes the sensitivity functions and optimum control of a transport and logistics process model. It explains the fundamental model of controlling safe ship movement as a differential game, and optimizing control algorithms through multi-matrix game and multi-stage positioning game. The sensitivity features for controlling safe ship in actual collision scenario are described in relation to inaccurate information of process position and variations in its varables, based on the determination of computer simulation algorithms in Matlab/Simulink software.

6

Analysis of optimization methods in marine objects control synthesis

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Lisowski J.

Journal of Automation, Electronics and Electrical Engineering

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2020

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tom Vol. 2, No. 1

25--30

EN

The article presents four main chapters that allow you to formulate an optimization task and choose a method for solving it from static and dynamic optimization methods to single-criterion and multi-criteria optimization. In the group of static optimization methods, the methods are without constraints and with constraints, gradient and non-gradient and heuristic. Dynamic optimization methods are divided into basic - direct and indirect and special. Particular attention has been paid to multicriteria optimization in single-object approach as static and dynamic optimization, and multi-object optimization in game control scenarios. The article shows not only the classic optimization methods that were developed many years ago, but also the latest in the field, including, but not limited to, particle swarms.

PL

W artykule przedstawiono cztery główne rozdziały, które pozwalają sformułować zadanie optymalizacji i wybrać metodę jego rozwiązania, od metod optymalizacji statycznej i dynamicznej do optymalizacji jedno i wielokryterialnej. W grupie metod optymalizacji statycznej metody te są bez ograniczeń i z ograniczeniami, gradientowe i bez gradientowe oraz heurystycznie. Metody optymalizacji dynamicznej dzielą się na podstawowe - bezpośrednie i pośrednie oraz specjalne. Szczególną uwagę zwrócono na optymalizację wielokryterialną w podejściu do jednego obiektu jako optymalizację statyczną i dynamiczną oraz optymalizację wielu obiektów w scenariuszach sterowania rozgrywającego. Artykuł pokazuje nie tylko klasyczne metody optymalizacji, które zostały opracowane wiele lat temu, ale także najnowsze w tej dziedzinie, w tym między innymi metody roju cząstek.