What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Leandro dos Santos Coelho mostly deals with Mathematical optimization, Particle swarm optimization, Metaheuristic, Multi-swarm optimization and Evolutionary computation.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Simulation and Economic dispatch.
His research in Particle swarm optimization intersects with topics in Probability distribution, Chaotic, Local search, Heuristic and Gaussian.
The various areas that Leandro dos Santos Coelho examines in his Metaheuristic study include Firefly algorithm and Ant colony.
His Evolutionary computation research focuses on Swarm intelligence and how it relates to Swarm behaviour and Premature convergence.
The Evolutionary algorithm study combines topics in areas such as Nonlinear programming, Genetic algorithm, Differential evolution, Multi-objective optimization and Benchmark.
His most cited work include:
- Multi-objective grey wolf optimizer (413 citations)
- Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems (253 citations)
- Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches (219 citations)
What are the main themes of his work throughout his whole career to date?
Leandro dos Santos Coelho spends much of his time researching Mathematical optimization, Metaheuristic, Particle swarm optimization, Differential evolution and Artificial intelligence.
His study in Mathematical optimization concentrates on Optimization problem, Evolutionary algorithm, Multi-swarm optimization, Multi-objective optimization and Evolutionary computation.
His study in Metaheuristic is interdisciplinary in nature, drawing from both Global optimization, Electric power system and Benchmark.
His Particle swarm optimization research is multidisciplinary, incorporating elements of Swarm behaviour, Gaussian and Chaotic.
His Differential evolution study incorporates themes from Economic dispatch, Genetic algorithm, Meta-optimization and Robustness.
His Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
He most often published in these fields:
- Mathematical optimization (54.17%)
- Metaheuristic (30.15%)
- Particle swarm optimization (26.23%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (21.32%)
- Mathematical optimization (54.17%)
- Support vector machine (8.33%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Mathematical optimization, Support vector machine, Artificial neural network and Machine learning.
In the subject of general Artificial intelligence, his work in Feature, Transfer of learning and Fuzzy inference system is often linked to Humpback whale, thereby combining diverse domains of study.
His research investigates the connection with Mathematical optimization and areas like Hyperparameter which intersect with concerns in Gaussian process.
His work deals with themes such as Mean absolute percentage error, Data mining, Ensemble learning, Ranking and Random forest, which intersect with Support vector machine.
His biological study deals with issues like Nonlinear system, which deal with fields such as Micromanipulator, Acoustics, Cantilever and Piezoelectricity.
As a part of the same scientific family, Leandro dos Santos Coelho mostly works in the field of Multi-objective optimization, focusing on Evolutionary algorithm and, on occasion, Multilayer perceptron, Genetic algorithm and Maximum power principle.
Between 2019 and 2021, his most popular works were:
- Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. (122 citations)
- Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. (122 citations)
- Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series (55 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Support vector machine, Random forest, Artificial neural network, Adaptive neuro fuzzy inference system and Ranking.
His studies deal with areas such as Ensemble learning, Gradient boosting and Extreme learning machine as well as Support vector machine.
His Artificial neural network research is multidisciplinary, incorporating elements of Algorithm, Nonlinear system and System identification.
His Adaptive neuro fuzzy inference system research includes themes of Wavelet and Data mining.
His research in Ranking intersects with topics in Econometrics, Regression and Time series.
Swarm intelligence is a primary field of his research addressed under Mathematical optimization.
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