E. J. Solteiro Pires
University of Trás-os-Montes and Alto Douro
78 Papers
237 Citations
E. J. Solteiro Pires is an academic researcher from University of Trás-os-Montes and Alto Douro. The author has contributed to research in topics: Particle swarm optimization & Genetic algorithm. The author has an hindex of 15, co-authored 67 publications.
Chat about Author
Papers
Particle swarm optimization with fractional-order velocity
E. J. Solteiro Pires,J. A. Tenreiro Machado,P. B. de Moura Oliveira,J. Boaventura Cunha,Luís Mendes +4 more
TL;DR: A novel method for controlling the convergence rate of a particle swarm optimization algorithm using fractional calculus (FC) concepts and the FC demonstrates a potential for interpreting evolution of the algorithm and to control its convergence.
Optimal Cable Design of Wind Farms: The Infrastructure and Losses Cost Minimization Case
TL;DR: In this article, the optimal design of the cable network interconnecting the turbines to the substation aiming to minimize not only the infrastructure cost but also the cost of the energy losses in the cables is addressed.
63
From single to many-objective PID controller design using particle swarm optimization
TL;DR: Simulation results are presented showing the effectiveness of the proposed PI-PID design techniques, in comparison with both classic and optimization based methods.
40
Multi-objective maximin sorting scheme
E. J. Solteiro Pires,P. B. de Moura Oliveira,J. A. Tenreiro Machado +2 more
- 09 Mar 2005
TL;DR: This paper proposes a new variant for the elitist selection operator to the NSGA-II algorithm, which promotes well distributed non-dominated fronts and replaces the crowding distance method by a maximin technique.
Robot Trajectory Planning Using Multi-objective Genetic Algorithm Optimization
E. J. Solteiro Pires,J. A. Tenreiro Machado,P. B. de Moura Oliveira +2 more
- 26 Jun 2004
TL;DR: A multi-objective genetic algorithm is proposed to address the problem of generating manipulator trajectories considering multiple objectives and obstacle avoidance, and results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization.
36