A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

Chen, Zhen-Yao

doi:10.1007/s44196-022-00130-4

Int J Comput Intell Syst

2022

DOI: 10.1007/s44196-022-00130-4

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A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

Zhen-Yao Chen

Abstract: This research attempts to reinforce the cultivating expression of radial basis function neural network (RBFnet) through computational intelligence (CI) and swarm intelligence (SI) learning methods. Consequently, the artificial immune system (AIS) and ant colony optimization (ACO) approaches are utilized to cultivate RBFnet for function approximation issue. The proposed hybridization of AIS and ACO approaches optimization (HIAO) algorithm combines the complementarity of exploitation and exploration to realize p… Show more

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Cited by 6 publications

References 83 publications

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Sustainability performance assessment of freight transportation modes using an integrated decision-making framework based on m-generalized q-neutrosophic sets

Görçün,

Tirkolaee,

Aytekin

et al. 2024

Artif Intell Rev

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The freight transport industry is one of the primary sectors responsible for excessive energy consumption and greenhouse gas emissions. Restructuring international and domestic freight transport chains based on sustainability and green transportation is critical for practitioners and policymakers to reduce pressure on the logistics and transportation industries. This study aims to develop a mathematical model for selecting the most appropriate transportation type, and accordingly, the optimal route in transportation operations to improve the sustainability performance of the freight transportation industry. Therefore, the main goal is to choose the most suitable route and transportation type which contributes to create a more eco-friendly and sustainable transportation system. For this purpose, Neutrosophic Number-based Delphi (NN-Delphi), m-Generalized q-Neutrosophic Sets (mGqNSs)-based Stepwise Weight Assessment Ratio Analysis (MGqNS-SWARA) and mGqNSs-based Additive Ratio Assessment (mGqNS-ARAS) are developed and implemented to set the influential criteria, compute the weights of these criteria, and identify the sustainability performance of the freight mode variants, respectively. According to the final results, "Cargo security" and "Accident rates" are the most important criteria with a relative importance score of 0.0237, contributing to the sustainability of load transport modes. Moreover, "Maritime Transport Mode" is identified as the most sustainable transportation type with a relative importance score of 0.7895. Finally, it is revealed that there is a positive relationship between maritime transport and sustainability.

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Sustainability performance assessment of freight transportation modes using an integrated decision-making framework based on m-generalized q-neutrosophic sets

Görçün,

Tirkolaee,

Aytekin

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

A blending ensemble learning model for crude oil price forecasting

Hasan,

Abedin,

Hajek

et al. 2024

Ann Oper Res

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To efficiently capture diverse fluctuation profiles in forecasting crude oil prices, we here propose to combine heterogenous predictors for forecasting the prices of crude oil. Specifically, a forecasting model is developed using blended ensemble learning that combines various machine learning methods, including k-nearest neighbor regression, regression trees, linear regression, ridge regression, and support vector regression. Data for Brent and WTI crude oil prices at various time series frequencies are used to validate the proposed blending ensemble learning approach. To show the validity of the proposed model, its performance is further benchmarked against existing individual and ensemble learning methods used for predicting crude oil price, such as lasso regression, bagging lasso regression, boosting, random forest, and support vector regression. We demonstrate that our proposed blending-based model dominates the existing forecasting models in terms of forecasting errors for both short- and medium-term horizons.

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Swarm Exploration Mechanism-Based Distributed Water Wave Optimization

Yang

Zhang

et al. 2023

Int J Comput Intell Syst

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Using sparrow search hunting mechanism to improve water wave algorithm (WWOSSA), which combines the water wave optimization (WWO) algorithm and the sparrow search algorithm (SSA), has good optimization ability and fast convergence speed. However, it still suffers from insufficient exploration ability and is easy to fall into local optimum. In this study, we propose a new algorithm for distributed population structure, called swarm exploration mechanism-based distributed water wave optimization (DWSA). In DWSA, an information exchange component and an optimal individual evolution component are designed to improve information exchange between individuals. This multi-part information interaction and distributed population structure algorithm can help the population algorithm to establish a balance between exploitation and exploration more effectively. We contrast DWSA with the original algorithms WWOSSA and other meta-heuristics in order to show the effectiveness of DWSA. The test set consists of 22 actual optimization issues from the CEC2011 set and 29 benchmark functions from the CEC2017 benchmark functions. In addition, an experimental comparison of the parameter values introduced in DWSA is included. According to experimental results, the proposed DWSA performs substantially better than its competitors. Assessments of the population diversity and landscape search trajectory also confirmed DWSA’s outstanding convergence.

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A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

Cited by 6 publications

References 83 publications

Sustainability performance assessment of freight transportation modes using an integrated decision-making framework based on m-generalized q-neutrosophic sets

Sustainability performance assessment of freight transportation modes using an integrated decision-making framework based on m-generalized q-neutrosophic sets

A blending ensemble learning model for crude oil price forecasting

Swarm Exploration Mechanism-Based Distributed Water Wave Optimization

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