Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species [1]. Artificial ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components. Since the first ACO algorithm has been proposed in 1991, this algorithmic method has attracted a large number of researchers and in the meantime it has reached a significant level of maturity. In fact, ACO is now a well-established search technique for tackling a wide variety of computationally hard problems.

Ant Colony Optimization

Multi-step-ahead forecast of reservoir water availability with improved quantum-based GWO coupled with the AI-based LSSVM model

Global solar radiation prediction: Application of novel hybrid data-driven model

This article sheds light on the question of whether it is possible to create fairly accurate forecasts of real oil prices For this purpose, a multi-level machine learning model has been created to analyze several sources of heterogeneous data to predict future prices The article uses different types of data: market condition data, titles, and transaction data Then, they have been processed to be able to load them into the model The validation of the regression neural network results showed that the model is more accurate than in previous studies In fact, this paper presents an artificial neural network model that solves the problem of determining the most informative relationship between different types of oil price data

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Multi-source model of heterogeneous data analysis for oil price forecasting

An instance-oriented performance measure for classification

A general framework and guidelines for benchmarking computational intelligence algorithms applied to forecasting problems derived from an application domain-oriented survey

Mihaela Oprea

Papers

A general framework and guidelines for benchmarking computational intelligence algorithms applied to forecasting problems derived from an application domain-oriented survey