A5006394687- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Advanced Multi-Objective Optimization Algorithms
- Energy Load and Power Forecasting
- Neural Networks and Applications
- Advanced Control Systems Optimization
- Fuzzy Logic and Control Systems
- Electric Power System Optimization
- Fault Detection and Control Systems
- Advanced Control Systems Design
- Control Systems and Identification
- Heat Transfer and Optimization
- Optimal Power Flow Distribution
- Stock Market Forecasting Methods
- Advanced Algorithms and Applications
- Anomaly Detection Techniques and Applications
- Grey System Theory Applications
- Extremum Seeking Control Systems
- Artificial Immune Systems Applications
- Chaos control and synchronization
- Iterative Learning Control Systems
- Adaptive Control of Nonlinear Systems
- Building Energy and Comfort Optimization
- Smart Grid Energy Management
- Structural Health Monitoring Techniques
Universidade Federal do Paraná
2016-2025
Universidade Federal do Ceará
2025
Pontifícia Universidade Católica do Paraná
2015-2024
Universidade Federal do Pará
2016-2023
Pontifical Catholic University of Puerto Rico
2014-2023
Pontifícia Universidade Católica de São Paulo
2023
Université Laval
2022
Institutos Lactec
2021
Inesc P&D Brasil
2021
Universidade do Estado da Bahia
2012-2021
In this paper, a new kind of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving optimization tasks. The EHO method inspired by the herding behavior elephant group. nature, elephants belonging to different clans live together under leadership matriarch, and male will leave their family group when they grow up. These two behaviors can be modelled into following operators: clan updating operator separating operator. EHO, in each are...
The behavior of natural phenomena has become one the most popular sources for researchers to design optimization algorithms scientific, computing and engineering fields. As a result, lot nature-inspired have been proposed in last decades. Due numerous issues global process, new are always welcome this research field. This paper introduces Coyote Optimization Algorithm (COA), which is population based metaheuristic inspired on canis latrans species. It contributes with algorithmic structure...
In this paper, a new swarm-based metaheuristic algorithm, called elephant herding optimisation (EHO), is proposed for solving global tasks, which inspired by the behaviour of groups. nature, elephants belonging to different clans live together under leadership matriarch, and male will leave their family group when growing up. These two behaviours can be modelled into following operators: clan updating operator separating operator. EHO, are updated using its current position matriarch through...
Earthworms can aerate the soil with their burrowing action and enrich waste nutrients. Inspired by earthworm contribution in nature, a new kind of bio-inspired metaheuristic algorithm, called optimisation algorithm (EWA), is proposed this paper. The EWA method inspired two kinds reproduction (Reproduction 1 Reproduction 2) earthworms. generates only one offspring itself. 2 to generate or more than at time, successfully be done nine improved crossover operators. In addition, Cauchy mutation...
The significance of accurate short-term load forecasting (STLF) for modern power systems' efficient and secure operation is paramount. This task intricate due to cyclicity, non-stationarity, seasonality, nonlinear consumption time series data characteristics. rise accessibility in the industry has paved way machine learning (ML) models, which show potential enhance STLF accuracy. paper presents a novel hybrid ML model combining Gradient Boosting Regressor (GBR), Extreme (XGBoost), k-Nearest...
The cost of electricity and gas has a direct influence on the everyday routines people who rely these resources to keep their businesses running. However, value is strongly related spot market prices, arrival winter increased energy use owing demand for heating can lead an increase in prices. Approaches forecasting costs have been used recent years; however, existing models are not yet robust enough due competition, seasonal changes, other variables. More effective modeling approaches...
Insulators installed outdoors are vulnerable to the accumulation of contaminants on their surface, which raise conductivity and increase leakage current until a flashover occurs. To improve reliability electrical power system, it is possible evaluate development fault in relation thus predict whether shutdown may occur. This paper proposes use empirical wavelet transform (EWT) reduce influence non-representative variations combines attention mechanism with long short-term memory (LSTM)...
The predictive maintenance of electrical machines is a critical issue for companies, as it can greatly reduce costs, increase efficiency, and minimize downtime. In this paper, the predicting machine failures by possible anomalies in data addressed through time series analysis. are from sensor attached to an (motor) measuring vibration variations three axes: X (axial), Y (radial), Z (radial X). dataset used train hybrid convolutional neural network with long short-term memory (CNN-LSTM)...
Electrical power grid insulators installed outdoors are exposed to environmental conditions, such as the accumulation of contaminants on their surface. The increase surface conductivity insulators, increasing leakage current until there is a flashover. Evaluating in relation contamination level one way determine insulation condition. This paper evaluates time series from high-voltage laboratory experiment using porcelain pin-type insulators. Time forecasting performed with collection machine...