Elitist-opposition-based artificial electric field algorithm for higher-order neural network optimization and financial time series forecasting

Elitist-opposition-based artificial electric field algorithm for higher-order neural network optimization and financial time series forecasting

Abstract This study attempts to accelerate the learning ability of an artificial electric field algorithm (AEFA) by attributing it with two mechanisms: elitism and opposition-based learning. Elitism advances the convergence of the AEFA towards global optima by retaining the fine-tuned solutions obta...

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Bibliographic Details
Main Authors: Sarat Chandra Nayak, Satchidananda Dehuri, Sung-Bae Cho
Format: Article
Language:English
Published: SpringerOpen 2024-01-01
Series:Financial Innovation
Subjects:
AEFA
Elitism
Opposition-based learning, Improved AEFA
HONN, PSNN
FLANN
Financial forecasting
Online Access:https://doi.org/10.1186/s40854-023-00534-x

Internet

https://doi.org/10.1186/s40854-023-00534-x

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