Temat: optimization algorithms - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Comparative Study of Particle Swarm Optimization and Genetic Algorithms for Complex Mathematical Functions
Autorzy:: Valdez, F.
Melin, P.
Powiązania:: https://bibliotekanauki.pl/articles/384575.pdf
Data publikacji:: 2008
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: genetic algorithms
particle swarm optimization (PSO)
hybrid systems
optimization
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.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 1; 43-51
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Toward the best combination of optimization with fuzzy systems to obtain the best solution for the GA and PSO algorithms using parallel processing
Autorzy:: Valdez, Fevrier
Kawano, Yunkio
Melin, Patricia
Powiązania:: https://bibliotekanauki.pl/articles/384329.pdf
Data publikacji:: 2020
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: genetic algorithms
particle swarm optimization (PSO)
fuzzy logic
parallel processing
Opis:: In general, this paper focuses on finding the best configuration for PSO and GA, using the different migration blocks, as well as the different sets of the fuzzy systems rules. To achieve this goal, two optimization algorithms were configured in parallel to be able to integrate a migration block that allow us to generate diversity within the subpopulations used in each algorithm, which are: the particle swarm optimization (PSO) and the genetic algorithm (GA). Dynamic parameter adjustment was also performed with a fuzzy system for the parameters within the PSO algorithm, which are the following: cognitive, social and inertial weight parameter. In the GA case, only the crossover parameter was modified.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 1; 55-64
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Layout of functional modules and routing for preliminary design of automatic teller machines
Autorzy:: Inoue, K.
Masuyama, T.
Osaki, H.
Ito, T.
Powiązania:: https://bibliotekanauki.pl/articles/384515.pdf
Data publikacji:: 2007
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: Module Layout
routing
simultaneous optimization
genetic algorithms
Design Intention
island model
Opis:: In this study we address the preliminary design for the module layout and bill conveyance routes of automatic teller machines (ATMs). We determine a two-dimensional layout for the modules as below that are approximately rectangular if the ATM is viewed from the side. ATMs require the compact placement of modules within the chassis and conveyance routes that smoothly circulate bills. However, the intersection and overlapping of routes by which the bills are conveyed in opposite directions are not allowed. Applying the bottom-left method and route-design-oriented packing method to the layout of the modules and the direction-oriented maze routing expediting branching and interflow of routes to the bill conveyance route, the application orders are optimized simultaneously using genetic algorithms (GAs). Results show that suitable designs for the ATM including the case when modules are selected as well as placed are achievable using the above simultaneous optimization. The design intention is expressible by changing the weights associated with chassis dimensions, route lengths and the number of route bends, which compose the objective function. The proposed method is useful for efficiently advancing the preliminary design of ATMs. Finally, if island models pursuing individual targets are used along with a GA, the design becomes even more efficient.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2007, 1, 4; 30-40
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Development of a modified ant colony algorithm for order scheduling in food processing plants
Autorzy:: Korobiichuk, Igor
Hrybkov, Serhii
Seidykh, Olga
Ovcharuk, Volodymyr
Ovcharuk, Andrii
Powiązania:: https://bibliotekanauki.pl/articles/2204558.pdf
Data publikacji:: 2022
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: order fulfillment planning
modified ant colony algorithm
efficiency of the algorithms
optimization
food industry
Opis:: This developed modified ant colony algorithm includes an additional improvement with local optimization methods, which reduces the time required to find a solution to the problem of optimization of combinatorial order sequence planning in a food enterprise. The planning problem requires consideration of a number of partial criteria, constraints, and an evaluation function to determine the effectiveness of the established version of the order fulfillment plan. The partial criteria used are: terms of storage of raw materials and finished products, possibilities of occurrence and processing of substandard products, terms of manufacturing orders, peculiarities of fulfillment of each individual order, peculiarities of use of technological equipment, expenses for storage and transportation of manufactured products to the end consumer, etc. The solution of such a problem is impossible using traditional methods. The proposed algorithm allows users to build and reconfigure plans, while reducing the time to find the optimum by almost 20% compared to other versions of algorithms.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 1; 53--61
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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