Temat: particle swarm optimization

Skocz do pozycji: 1.

Tytuł:: Implementation of multithreaded Particle Swarm Optimization algorithm
Implementacja wielowątkowego algorytmu Particle Swarm Optimization
Autorzy:: Rachwał, Szczepan
Opis:: This paper has been written with an intention to introduce the recipent to a problem of optimizing multidimensional functions. It covers a theoretical approach to this issue, defining the concept. The main goal of this paper is to present a version of Particle Swarm Optimization algorithm which has been improved in comparison to the original one. Both the formal definition, as well as a practical usage will be covered. A series of tests on different functions will be performed to target typical issues with Particle Swarm Optimization and their behavior in the proposed multithread algorithm version.A computer program written in Java is an integral part of this paper. It implements the proposed new version of the Particle Swarm Optimization algorithm, to demonstrate this new approach and its strengths.
Niniejsza praca ma na celu przybliżenie tematyki optymalizacji funkcji wielu zmiennych. Głównym jej założeniem jest przedstawienie optymalizacji skomplikowanych funkcji wielowymiarowych za pomocą ulepszonego algorytmu Particle Swarm Optimization. Zostanie zaprezentowana teoretyczna część tego zagadnienia, jak również działanie algorytmu na przykładach. Przybliżone zostaną również konkretne typy funkcji pozwalające zaprezentować mocne i słabe strony Particle Swarm Optimization.Ponadto, integralną częścią pracy jest również program komputerowy napisany w języku Java, w którym zostanie zaimplementowane wspomniane nowe podejście do zagadnienia Particle Swarm Optimization, eliminujące niektóre słabości oryginalnej wersji algorytmu.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Skocz do pozycji: 2.

Tytuł:: A hybrid PSO approach for solving non-convex optimization problems
Autorzy:: Ganesan, T.
Vasant, P.
Elamvazuthy, I.
Tematy:: Kuhn-Tucker conditions (KT)
non-convex optimization
particle swarm optimization (PSO)
semi-classical particle swarm optimization (SPSO); Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/229756.pdf Link otwiera się w nowym oknie
Opis:: The aim of this paper is to propose an improved particle swarm optimization (PSO) procedure for non-convex optimization problems. This approach embeds classical methods which are the Kuhn-Tucker (KT) conditions and the Hessian matrix into the fitness function. This generates a semi-classical PSO algorithm (SPSO). The classical component improves the PSO method in terms of its capacity to search for optimal solutions in non-convex scenarios. In this work, the development and the testing of the refined the SPSO algorithm was carried out. The SPSO algorithm was tested against two engineering design problems which were; ‘optimization of the design of a pressure vessel’ (P1) and the ‘optimization of the design of a tension/compression spring’ (P2). The computational performance of the SPSO algorithm was then compared against the modified particle swarm optimization (PSO) algorithm of previous work on the same engineering problems. Comparative studies and analysis were then carried out based on the optimized results. It was observed that the SPSO provides a better minimum with a higher quality constraint satisfaction as compared to the PSO approach in the previous work.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Comparative Study of Particle Swarm Optimization and Genetic Algorithms for Complex Mathematical Functions
Autorzy:: Valdez, F.
Melin, P.
Tematy:: genetic algorithms
particle swarm optimization (PSO)
hybrid systems
optimization; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Powiązania:: https://bibliotekanauki.pl/articles/384575.pdf Link otwiera się w nowym oknie
Opis:: The Particle Swarm Optimization (PSO) and the Genetic Algorithms (GA) have been used successfully in solving problems of optimization with continuous and combinatorial search spaces. In this paper the results of the application of PSO and GAs for the optimization of mathematical functions are presented. These two methodologies have been implemented with the goal of making a comparison of their performance in solving complex optimization problems. This paper describes a comparison between a GA and PSO for the optimization of complex mathematical functions.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Celestial navigation fix based on particle swarm optimization
Autorzy:: Tsou, M.-C.
Tematy:: particle swarm optimization (PSO)
Celestial navigation
Intercept method; Pokaż więcej
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Powiązania:: https://bibliotekanauki.pl/articles/258524.pdf Link otwiera się w nowym oknie
Opis:: A technique for solving celestial fix problems is proposed in this study. This method is based on Particle Swarm Optimization from the field of swarm intelligence, utilizing its superior optimization and searching abilities to obtain the most probable astronomical vessel position. In addition to being applicable to two-body fix, multi-body fix, and high-altitude observation problems, it is also less reliant on the initial dead reckoning position. Moreover, by introducing spatial data processing and display functions in a Geographical Information System, calculation results and chart work used in Circle of Position graphical positioning can both be integrated. As a result, in addition to avoiding tedious and complicated computational and graphical procedures, this work has more flexibility and is more robust when compared to other analytical approaches.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Collision-free autonomous robot navigation in unknown environments utilizing PSO for path planning
Autorzy:: Krell, Evan
Sheta, Alaa
Balasubramanian, Arun Prassanth Ramaswamy
King, Scott A.
Tematy:: mobile robot
particle swarm optimization (PSO)
path planning; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/91555.pdf Link otwiera się w nowym oknie
Opis:: The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Computer-aided system for layout of fire hydrants on boards designed vessel using the Particle Swarm Optimization algorithm
Autorzy:: Gomułka, Piotr
Tematy:: ship
fire hydrant
design
layout
particle swarm optimization; Pokaż więcej
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Powiązania:: https://bibliotekanauki.pl/articles/34600515.pdf Link otwiera się w nowym oknie
Opis:: The functional layout of fire safety equipment in technical spaces of ships is a time-consuming process. When designing a ship fire protection system, the designer must manually position each system component in such a way as to meet the requirements of regulations arising from the technical specification, various legal regulations of maritime conventions and classification societies of the vessel to be designed. Layout of fire hydrants assisted by a computer that is based on pre-defined criteria and various constraints could significantly support the designer in working easier and faster. This paper presents a prototype computer-aided design system that enables optimal placement of fire hydrants using the metaheuristic Particle Swarm Optimization (PSO) algorithm. This algorithm was used in Rhinoceros 3D software with its Grasshopper plugin for visualizing the arrangement of fire safety equipment. Various solution arrangements compared with the fire hydrant placement in real ships are illustrated by a case study. Demonstrating how design work can be facilitated and what potential benefits can be achieved are presented as well.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: A hybrid PSO-GA algorithm for Reversible Circuits Synthesis
Hybrydowy algorytm PSO-GA dla syntezy układów odwracalnych
Autorzy:: Podlaski, K.
Tematy:: reversible circuits
reversible logic synthesis
particle swarm optimization (PSO)
genetic algorithms
układy odwracalne
synteza układów odwracalnych
particle swarm optimization
algorytmy genetyczne; Pokaż więcej
Wydawca:: Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Powiązania:: https://bibliotekanauki.pl/articles/153468.pdf Link otwiera się w nowym oknie
Opis:: In the domain of Reversible Circuits there is still lack of good synthesis algorithms. There are many heuristic propositions, unfortunately, their results for a given reversible function usually are circuits far from optimal implementations. There are some propositions of using Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) for this purpose. In this paper a new hybrid PSO-GA algorithm is proposed. Comparison of the proposed algorithm with the existing ones gives promising results.
W dobie poszukiwania układów cyfrowych o niskim zużyciu energii układy odwracalne stanowią ciekawą alternatywę dla aktualnie stosowanych układów cyfrowych. Jednym z najistotniejszych zagadnień w dziedzinie budowy układów cyfrowych jest synteza układu reprezentującego zadaną funkcję. Niestety do dzisiaj nie ma dobrych rozwiązań w dziedzinie syntezy układów odwracalnych, istniejące rozwiązania są bardzo czasochłonne bądź generują układy o dużej redundancji. Ciekawą alternatywą dla obecnie stosowanych metod heurystycznych jest wykorzystanie algorytmów ewolucyjnych np. Particle Swarm Optimization (PSO) lub algorytmów genetycznych (GA). W niniejszym artykule zaproponowano nowy hybrydowy algorytm PSO-GA dostosowany do syntezy odwracalnych układów cyfrowych. Stworzony algorytm zastosowano do syntezy układów dla wybranych funkcji testowych (tzw. benchmarków) a wyniki porównano z wynikami otrzymywanymi za pomocą algorytmów heurystycznych. Wygenerowane układy okazały się mniej redundantne niż układy otrzymane w syntezie metodami heurystycznymi.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Bainite transformation time model optimization for Austempered Ductile Iron with the use of heuristic algorithms
Autorzy:: Olejarczyk-Wożeńska, Izabela
Opaliński, Andrzej
Mrzygłód, Barbara
Regulski, Krzysztof
Kurowski, Wojciech
Tematy:: heuristic optimization
bainite
ADI
Particle Swarm Optimization
Evolutionary Optimization Algorithm; Pokaż więcej
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Powiązania:: https://bibliotekanauki.pl/articles/29520068.pdf Link otwiera się w nowym oknie
Opis:: The paper presents the application of heuristic optimization methods in identifying the parameters of a model for bainite transformation time in ADI (Austempered Ductile Iron). Two algorithms were selected for parameter optimization – Particle Swarm Optimization and Evolutionary Optimization Algorithm. The assumption of the optimization process was to obtain the smallest normalized mean square error (objective function) between the time calculated on the basis of the identified parameters and the time derived from the experiment. As part of the research, an analysis was also made in terms of the effectiveness of selected methods, and the best optimization strategies for the problem to be solved were selected on their basis.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: The multi-constrained multicast routing improved by hybrid bacteria foraging-particle swarm optimization
Autorzy:: Sahoo, Satya Prakash
Kabat, Manas Ranjan
Tematy:: QoS routing
multicasting
bacteria foraging optimization
particle swarm optimization (PSO); Pokaż więcej
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Powiązania:: https://bibliotekanauki.pl/articles/305674.pdf Link otwiera się w nowym oknie
Opis:: To solve multicast routing under multiple constraints, it is required to generate a multicast tree that ranges from a source to the destinations with minimum cost subject to several constraints. In this paper, PSO has been embedded with BFO to improve the convergence speed and avoid premature convergence that will be used for solving QoS multicast routing problem. The algorithm proposed here generates a set of delay compelled links to every destination present in the multicast group. Then the Bacteria Foraging Algorithm (BFA) selects the paths to all the destinations sensibly from the set of least delay paths to construct a multicast tree. The robustness of the algorithm being proposed had been established through the simulation. The efficiency and effectiveness of the algorithm being proposed was validated through the comparison study with other existing meta-heuristic algorithms. It shows that our proposed algorithm IBF-PSO outperforms its competitive algorithms.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Research on the mill feeding system of an elastic variable universe fuzzy control based on particle swarm optimization algorithm
Autorzy:: Tian, Niu
Huang, Songwei
He, Lifang
Du, Lingpan
Yang, Sheping
Huang, Bin
Tematy:: fuzzy control
contraction-expansion factor
particle swarm optimization; Pokaż więcej
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Powiązania:: https://bibliotekanauki.pl/articles/24085898.pdf Link otwiera się w nowym oknie
Opis:: The grinding process in the concentrator is a part of the largest energy consumption, but also the most likely to cause a waste of resources, so the optimization of the grinding process is a very important link.The traditional fuzzy controller relies solely on the expert knowledge summary to construct control rules, which can cause significant steady-state errors in the model. In order to solve the above problem, this paper proposes an elastic variable universe fuzzy control based on Particle Swarm Optimization (PSO) algorithm. The elastic universe fuzzy control model does not need precise fuzzy rules, but only needs to input the general trend of the rules, and the division of the universe is performed by the contraction-expansionfactor. The control performance is directly related to the contraction-expansionfactor, so this article also proposes using particle swarm optimization to optimize the scaling factor to achieve the optimal value. Finally, simulation models of traditional fuzzy control and elastic universe fuzzy control of feeding system of mill were built using Python to verify the control effect. Itssimulation results show that the time of the reaction of the fuzzy control system in the elastic variable theory universe based on particle swarm optimization was shorter by 34.48% comparing to the traditional one. Elastic variable universe fuzzy control based on particle swarm optimization (PSO) effectively improved the control accuracy of the mill feeding system and improved the response speed of the system to a certain extent.
Dostawca treści:: Biblioteka Nauki

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