P. B. de Moura Oliveira

Manipulator trajectory planning using a MOEA

Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non-trivial optimization problem. In this paper a multi-objective genetic algorithm based technique is proposed to address this problem. Multiple criteria are optimized considering up to five simultaneous objectives. Simulation results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization. A subsequent analysis of the spread and solutions distribution along the converged non-dominated Pareto front is carried out, in terms of the achieved diversity.

Fractional order dynamics in a GA planner

This work addresses the signal propagation and the fractional-order dynamics during, the evolution of a genetic algorithm (GA), for generating a robot manipulator trajectory. The GA objective is to minimize the trajectory space/time ripple without exceeding the torque requirements. In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations and the corresponding fitness variations are evaluated. The chaos-like noise and the input/output signals are studied revealing a fractional-order dynamics, characteristic of a long-term system memory.

Robot Trajectory Planning Using Multi-objective Genetic Algorithm Optimization

Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non trivial optimization problem. In this paper a multi-objective genetic algorithm is proposed to address this problem. Multiple criteria are optimized up to five simultaneous objectives. Simulations results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization. A subsequent analysis of the solutions distribution along the converged non-dominated Pareto front is carried out, in terms of the achieved diversity.

Multi-objective maximin sorting scheme

Obtaining a well distributed non-dominated Pareto front is one of the key issues in multi-objective optimization algorithms. This paper proposes a new variant for the elitist selection operator to the NSGA-II algorithm, which promotes well distributed non-dominated fronts. The basic idea is to replace the crowding distance method by a maximin technique. The proposed technique is deployed in well known test functions and compared with the crowding distance method used in the NSGA-II algorithm. This comparison is performed in terms of achieved front solutions distribution by using distance performance indices.

Dynamical modelling of a genetic algorithm

This work addresses the signal propagation and the fractional-order dynamics during the evolution of a genetic algorithm (GA). In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations during some generations and the corresponding fitness variations are evaluated. Three distinct fitness functions are used to study their influence in the GA dynamics. The input and output signals are studied revealing a fractional-order dynamic evolution, characteristic of a long-term system memory.

Greenhouse air temperature control using the particle swarm optimisation algorithm

Abstract The particle swarm optimisation algorithm is proposed as a new method to design a model based predictive controller subject to restrictions. Its performance is compared with the one obtained by using a genetic algorithm for the environmental temperature control of a greenhouse. Controller outputs are computed in order to optimise future behaviour of the greenhouse environment, regarding set-point tracking and minimisation of the control effort over a prediction horizon of one hour with a one-minute sampling period.

Road Tunnels Lighting using Genetic Algorithms

This paper presents a tool for automating the design of road tunnels lighting systems. The tunnel lighting system must guarantee some minimal luminance values in order to ensure a easy driving and visual perception. The lights distribution, in different tunnel zones, is obtained in the proposed technique by using a genetic algorithm. The developed software framework automatically selects the best light type and its localization, according to a specified design objective, along the tunnel independently of the light manufacturer.

Fractional Order Dynamics in a Particle Swarm Optimization Algorithm

This article reports the study of fractional dynamics during the evolution of a particle swarm optimization (PSO) algorithm. Some initial swarm particles are randomly changed, for stimulating the system response, and its effect is compared with a non-perturbed reference. The perturbation effect in the PSO evolution is observed in the perspective of the fitness time behavior of the best particle. The dynamics is represented through the median of a sample of experiments, while adopting the Fourier analysis for describing the phenomena. The influence of the PSO parameters influence upon the global dynamics is also analyzed.

Teaching automation and control with App Inventor applications

This paper presents an experiment concerning the development of two mobile devices applications with the App Inventor 2 for Android operating systems. These applications are intended to support teaching and learning activities in Industrial Automation and Control courses, particularly concerning logic control, logic controller programming and process control. While the reported applications: eLogicum and Automatum are yet in an early development stage, this paper aims to motivate teachers and students to a wider use of mobile devices in the context of university teaching and learning processes. Both applications are reported, focusing in the more relevant development issues concerning the App Inventor 2 use.

Many-objective optimization with corner-based search

The performance of multi-objective evolutionary algorithms can severely deteriorate when applied to problems with 4 or more objectives, called many-objective problems. For Pareto dominance based techniques, available information about some optimal solutions can be used to improve their performance. This is the case of corner solutions. This work considers the behaviour of three multi-objective algorithms [Non-dominated sorting genetic algorithm (NSGA-II), Speed-constrained multi-objective particle swarm optimization (SMPSO) and generalized differential evolution (GDE3)] when corner solutions are inserted into the population at different evolutionary stages. The problem of finding corner solutions is addressed by proposing a new algorithm based in multi-objective particle swarm optimization (MOPSO). Results concerning the behaviour of the aforementioned algorithms in five benchmark problems (DTLZ1-5) and respective analysis are presented.