Temat: swarm optimization - Prolib Integro

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:: An Ant Algorithm for the Sudoku Problem
Autorzy:: Schiff, K.
Tematy:: swarm optimization
Sudoku puzzle; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Powiązania:: https://bibliotekanauki.pl/articles/384822.pdf Link otwiera się w nowym oknie
Opis:: In this paper an ant algorithm for the Sudoku problem is presented. This is the first ant algorithm enabling discovery of an optimal solution to the Sudoku puzzle for 100% of investigated cases. The Sudoku is a one of many combinatorial optimisation problems, as well as an NPcomplete problem, hence an ant algorithm which constructs an optimal solution as a meta-heuristic method is important for this problem.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

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: 4.

Tytuł:: Improvements to Glowworm Swarm Optimization algorithm
Ulepszenia algorytmu Glowworm Swarm Optimization
Autorzy:: Oramus, P.
Tematy:: inteligencja roju
optymalizacja
swarm intelligence
glowworm swarm optimization
multimodal function optimization; Pokaż więcej
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Powiązania:: https://bibliotekanauki.pl/articles/305567.pdf Link otwiera się w nowym oknie
Opis:: Glowworm Swarm Optimization algorithm is applied for the simultaneous capture of multiple optima of multimodal functions. The algorithm uses an ensemble of agents, which scan the search space and exchange information concerning a fitness of their current position. The fitness is represented by a level of a luminescent quantity called luciferin. An agent moves in direction of randomly chosen neighbour, which broadcasts higher value of the luciferin. Unfortunately, in the absence of neighbours, the agent does not move at all. This is an unwelcome feature, because it diminishes the performance of the algorithm. Additionally, in the case of parallel processing, this feature can lead to unbalanced loads. This paper presents simple modifications of the original algorithm, which improve performance of the algorithm by limiting situations, in which the agent cannot move. The paper provides results of comparison of an original and modified algorithms calculated for several multimodal test functions.
Algorytm Glowworm Swarm Optimization jest stosowany do równoczesnego odnajdywania wielu optimów funkcji multimodalnych. Algorytm używa zespołu agentów przeszukujących przestrzeń poszukiwań i wymieniających się informacjami o wartości funkcji przystosowania w danym położeniu. Funkcja przystosowania jest reprezentowana przez poziom emitującego światło pigmentu - lucyferyny. Agenci poruszają się w kierunku losowo wybranego sąsiada, który rozgłasza wyższą wartość poziomu lucyferyny. Niestety w przypadku braku sąsiadów agent nie porusza się wcale. Stanowi to niepożądaną cechę algorytmu ograniczającą jego wydajność. W przypadku przetwarzania równoległego cecha ta może prowadzić do niezrównoważenia obciążenia. Praca ta przedstawia proste modyfikacje oryginalnego algorytmu zwiększające jego wydajność poprzez ograniczanie liczby takich sytuacji, w których agent nie może się poruszyć. Przedstawione zostały wyniki porównania pracy oryginalnego i zmodyfikowanych algorytmów dla kilku funkcji testowych.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Application of the Bee Swarm Optimization BSO to the Knapsack Problem
Autorzy:: Sotelo-Figueroa, M. A.
Baltazar, B.
Carpio, J. M.
Tematy:: swarm optimization
PSO
BA
BSO
knapsack problem; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Powiązania:: https://bibliotekanauki.pl/articles/384879.pdf Link otwiera się w nowym oknie
Opis:: Swarm Intelligence is the part of Artificial Intelligence based on study of actions of individuals in various decentralized systems. The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel hybrid algorithm based in Bees Algorithm and Particle Swarm Optimization is applied to the Knapsack Problem. The Bee Algorithm is a new population-based search algorithm inspired by the natural foraging behavior of honey bees, it performs a kind of exploitative neighborhood search combined with random explorative search to scan the solution, but the results obtained with this algorithm in the Knapsack Problem are not very good. Although the combination of BA and PSO is given by BSO, Bee Swarm Optimization, this algorithm uses the velocity vector and the collective memories of PSO and the search based on the BA and the results are much better. We use the Greedy Algorithm, which it's an approximate algorithm, to compare the results from these metaheuristics and thus be able to tell which is which gives better results.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

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: 7.

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: 8.

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: 9.

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: 10.

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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