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About

About

E. J. Solteiro Pires received the B.Sc. degree in electrical engineering from the University of Coimbra, Coimbra, Portugal, in 1994, the M.Sc. degree in electrical and computer engineering from the University of Porto, Porto, Portugal, in 1999, and the Ph.D. degree from the University of Trás-os-Montes and Alto Douro (UTAD), Vila Real, Portugal, in 2006. He is currently an Auxiliary Professor with UTAD. His current research interests include evolutionary computation, soft computing, multiobjective problems, and robotics.

Interest
Topics
Details

Details

Publications

2020

Review of nature and biologically inspired metaheuristics for greenhouse environment control

Authors
Oliveira, PM; Pires, EJS; Boaventura Cunha, J; Pinho, TM;

Publication
Transactions of the Institute of Measurement and Control

Abstract
A significant number of search and optimisation techniques whose principles seek inspiration from nature and biology phenomena have been proposed in the last decades. These methods have been successfully applied to solve a wide range of engineering problems. This is also the case of greenhouse environment control, which has been incorporating this type of techniques into its design. This paper addresses evolutionary and bio-inspired methods in the context of greenhouse environment control. Algorithm principles for reference techniques are reviewed, namely: simulated annealing, genetic algorithm, differential evolution and particle swarm optimisation. The last three techniques are considered using single and multiple objective formulations. A review of these algorithms within greenhouse environment control applications is presented, considering single and multiple objective problems, as well as their current trends. © The Author(s) 2020.

2020

Path Planning for ground robots in agriculture: a short review

Authors
Santos, LC; Santos, FN; Solteiro Pires, EJS; Valente, A; Costa, P; Magalhaes, S;

Publication
2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

Abstract

2020

Entropy Based Grey Wolf Optimizer

Authors
Duarte, D; Moura Oliveira, PBd; Solteiro Pires, EJ;

Publication
Lecture Notes in Computer Science - Intelligent Data Engineering and Automated Learning – IDEAL 2020

Abstract

2020

Swarm-Based Design of Proportional Integral and Derivative Controllers Using a Compromise Cost Function: An Arduino Temperature Laboratory Case Study

Authors
Oliveira, PBD; Hedengren, JD; Pires, EJS;

Publication
Algorithms

Abstract
Simple and easy to use methods are of great practical demand in the design of Proportional, Integral, and Derivative (PID) controllers. Controller design criteria are to achieve a good set-point tracking and disturbance rejection with minimal actuator variation. Achieving satisfactory trade-offs between these performance criteria is not easily accomplished with classical tuning methods. A particle swarm optimization technique is proposed to design PID controllers. The design method minimizes a compromise cost function based on both the integral absolute error and control signal total variation criteria. The proposed technique is tested on an Arduino-based Temperature Control Laboratory (TCLab) and compared with the Grey Wolf Optimization algorithm. Both TCLab simulation and physical data show that satisfactory trade-offs between the performance and control effort are enabled with the proposed technique.

2019

Dynamic shannon performance in a multiobjective particle swarm optimization

Authors
Solteiro Pires, EJS; Tenreiro Machado, JAT; de Moura Oliveira, PBD;

Publication
Entropy

Abstract
Particle swarm optimization (PSO) is a search algorithm inspired by the collective behavior of flocking birds and fishes. This algorithm is widely adopted for solving optimization problems involving one objective. The evaluation of the PSO progress is usually measured by the fitness of the best particle and the average fitness of the particles. When several objectives are considered, the PSO may incorporate distinct strategies to preserve nondominated solutions along the iterations. The performance of the multiobjective PSO (MOPSO) is usually evaluated by considering the resulting swarm at the end of the algorithm. In this paper, two indices based on the Shannon entropy are presented, to study the swarm dynamic evolution during the MOPSO execution. The results show that both indices are useful for analyzing the diversity and convergence of multiobjective algorithms. © 2019 by the authors.

Supervised
thesis

2020

Classificação de doenças neurodegenerativas baseada na locomoção humana

Author
Filipa Daniela Azevedo da Silva

Institution
UP-FEUP

2019

Chat Bot -o diagmóstico de bolso

Author
Duarte Rui Afonso Gomes Tavares do Amaral

Institution
UTAD

2019

ADAPT – Plataforma Adaptativa de Ensino à Distância

Author
Eduardo Jorge Dinis Pratas

Institution
UTAD

2018

Análise da variabilidade da frequência cardíaca em indivíduos saudáveis e doentes

Author
Cristina Monteiro Pinto

Institution
UTAD

2018

Sistemas Baseados em casos: Aplicação à Saúde

Author
Stéfanie Maria da Costa Alves

Institution
UTAD