Temat: fireworks algorithm - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Fireworks Algorithm for Unconstrained Function Optimization Problems
Autorzy:: Baidoo, E.
Powiązania:: https://bibliotekanauki.pl/articles/117784.pdf
Data publikacji:: 2017
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Tematy:: Fireworks algorithm
Function optimization
swarm intelligence
Mathematical programming
Natural computing
Opis:: Modern real world science and engineering problems can be classified as multi-objective optimisation problems which demand for expedient and efficient stochastic algorithms to respond to the optimization needs. This paper presents an object-oriented software application that implements a firework optimization algorithm for function optimization problems. The algorithm, a kind of parallel diffuse optimization algorithm is based on the explosive phenomenon of fireworks. The algorithm presented promising results when compared to other population or iterative based meta-heuristic algorithm after it was experimented on five standard ben-chmark problems. The software application was implemented in Java with interactive interface which allow for easy modification and extended expe-rimentation. Additionally, this paper validates the effect of runtime on the al-gorithm performance.
Źródło:: Applied Computer Science; 2017, 13, 1; 61-74
1895-3735
Pojawia się w:: Applied Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Forecasting currency exchange rate time series with fireworks algorithm-based higher order neural network, with special attention to training data enrichment
Autorzy:: Sahu, Kishore Kumar
Nayak, Sarat Chandra
Behera, Himansu Sekhar
Powiązania:: https://bibliotekanauki.pl/articles/1839247.pdf
Data publikacji:: 2020
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: exchange rate
virtual data point
interpolation
artificial neural network
fireworks algorithm
functional link neural network
Opis:: Exchange rates are highly fluctuating by nature; thus, they are difficult to forecast. Artificial neural networks (ANNs) have proven to be better than statistical methods. Inadequate training data may lead the model to reach sub-optimal solutions, resulting in poor accuracy (as ANN-based forecasts are data-driven). To enhance forecasting accuracy, we suggests a method of enriching training datasets through exploring and incorporating virtual data points (VDPs) by an evolutionary method called the fireworks algorithm-trained functional link artificial neural network (FWA-FLN). The model maintains a correlation between current and past data, especially at the oscillation point on the time series. The exploration of a VDP and forecast of the succeeding term go consecutively by FWA-FLN. Real exchange rate time series are used to train and validate the proposed model. The efficiency of the proposed technique is related to other similarly trained models and produces far better prediction accuracy.
Źródło:: Computer Science; 2020, 21 (4); 463-488
1508-2806
2300-7036
Pojawia się w:: Computer Science
Dostawca treści:: Biblioteka Nauki

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