Abstract

Abstract
The performance of multi-objective evolutionary algorithms (MOEA) is severely deteriorated when applied to many-objective problems. For Pareto dominance based techniques, available information about 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 (NSGA-II, SMPSO and GDE3) when corner solutions are inserted into the population at different evolutionary stages. Corner solutions are found using specific algorithms. Preliminary results are presented concerning the behaviour of the aforementioned algorithms in five benchmark problems (DTLZ1-5).

Abstract
A new hybrid between the particle swarm optimization (PSO) and Boids is presented to design PID controllers applied to the mean arterial pressure control problem. While both PSO and Boids have been extensively studied separately, their hybridization potential is far from fully explored. The PSOBoids algorithm is proposed to perform both system identification and PID controller design. The advantage over a standard particle swarm optimization algorithm is the promotion of the diversity of the search procedure. Preliminary simulation results are presented. © Springer International Publishing Switzerland 2014.

Abstract
The particle swarm optimization (PSO), one of the most successful natural inspired algorithms, is revisited in the context of a proposal for a new teaching experiment. The problem considered is the open-loop step identification procedure, which is studied as an optimization problem. The PSO canonical algorithm main issues addressed within the proposed open-loop step identification experience are: the swarm random initialization methodology, the population size variation, and the inertia weight selection. The teaching experience learning outcomes are stated, simulation results presented, and feedback results from students analyzed. (c) 2011 Wiley Periodicals, Inc. Comput Appl Eng Educ 22:227-237, 2014; View this article online at ; DOI

Abstract
Multi-objective optimization inspired on genetic algorithms are population based search methods. The population elements, chromosomes, evolve using inheritance, mutation, selection and crossover mechanisms. The aim of these algorithms is to obtain a representative non-dominated Pareto front from a given problem. Several approaches to study the convergence and performance of algorithm variants have been proposed, particularly by accessing the final population. In this work, a novel approach by analyzing multi-objective algorithm dynamics during the algorithm execution is considered. The results indicate that Shannon entropy can be used as an algorithm indicator of diversity and convergence.

Abstract
This work presents an automatic tuning method for the discontinuous component of the Sliding Mode Generalized Predictive Controller (SMGPC) subject to constraints. The strategy employs Particle Swarm Optimization (PSO) to minimize a second aggregated cost function. The continuous component is obtained by the standard procedure, by Quadratic Programming (QP), thus yielding an online dual optimization scheme. Simulations and performance indexes for common process models in industry, such as nonminimum phase and time delayed systems, result in a better performance, improving robustness and tracking accuracy.