Gustavo Sánchez

HCS: A New Local Search Strategy for Memetic Multiobjective Evolutionary Algorithms

In this paper, we propose and investigate a new local search strategy for multiobjective memetic algorithms. More precisely, we suggest a novel iterative search procedure, known as the Hill Climber with Sidestep (HCS), which is designed for the treatment of multiobjective optimization problems, and show further two possible ways to integrate the HCS into a given evolutionary strategy leading to new memetic (or hybrid) algorithms. The pecularity of the HCS is that it is intended to be capable both moving toward and along the (local) Pareto set depending on the distance of the current iterate toward this set. The local search procedure utilizes the geometry of the directional cones of such optimization problems and works with or without gradient information. Finally, we present some numerical results on some well-known benchmark problems, indicating the strength of the local search strategy as a standalone algorithm as well as its benefit when used within a MOEA. For the latter we use the state of the art algorithms Nondominated Sorting Genetic Algorithm-II and Strength Pareto Evolutionary Algorithm 2 as base MOEAs.

A new memetic strategy for the numerical treatment of multi-objective optimization problems

In this paper we propose a novel iterative search procedure for multi-objective optimization problems. The iteration process -- though derivative free -- utilizes the geometry of the directional cones of such optimization problems, and is capable both of moving toward and along the (local) Pareto set depending on the distance of the current iterate toward this set. Next, we give one possible way of integrating this local search procedure into a given EMO algorithm resulting in a novel memetic strategy. Finally, we present some numerical results on some well-known benchmark problems indicating the strength of both the local search strategy as well as the new hybrid approach.

Multi-objective pole placement with evolutionary algorithms

Multi-Objective Evolutionary Algorithms (MOEA) have been succesfully applied to solve control problems. However, many improvements are still to be accomplished. In this paper a new approach is proposed: the Multi-Objective Pole Placement with Evolutionary Algorithms (MOPPEA). The design method is based upon using complex-valued chromosomes that contain information about closed-loop poles, which are then placed through an output feedback controller. Specific cross-over and mutation operators were implemented in simple but efficient ways. The performance is tested on a mixed multi-objective H2/H∞ control problem.

Solving Multi-Objective Linear Control Design Problems Using Genetic Algorithms

Abstract Two multi-objective genetic algorithms, an elitist version of MOGA and NSGA-II, were applied to solve two linear control design problems. The first was a H 2 problem with a PI controller structure, for a first order stable plant. The second was a mixed H 2 /H ∞ control problem. In both cases, three indicators were used to evaluate each algorithm performance: Set coverage, spread and hypervolume. It was found that NSGA-II shows better performance indicators. Moreover, for the second problem, a new controller representation was proposed with corresponding cross-over and mutation operators. This approach was able to find solutions as good as those previously published. The main advantage is that the stability restriction disappears, allowing to deal with an unconstrained optimization problem.

PID-Control Optimization Using Solis-Wets Algorithm And Laguerre System Identification

In this paper, a design method for PID controllers is proposed, based on the Solis-Wets random search algorithm and closed-loop Laguerre identification. The method is tested considering two sets of benchmark plants, previously considered by many other authors. Results show that this method is able to decrease the regulation cost function value considered, while decreasing the rising and settling time and increasing the frequency of the response when compared with an initial controller. The statistic values associated with the method show that it is not affected in a significantly way by the initial values, system order and time constant assumed in its implementation.

A low-cost control system for hydraulic applications

This paper discuss the main technical issues related with the development of a low-cost control system for hydraulic applications. A prototype of the device equipped with sensors was first tested and calibrated in the laboratory before operating it under actual field conditions. The control system was developed using exclusively free programs which can easily be downloaded on Internet and easy-to-find components, which can be purchased in any electronics store.

A NEW GLUCOSE REGULATION SYSTEM MODEL

The aim of this work was to develop a new mathematical model describing the human glucose regulation system, as an important step in order to design more efficient Diabetes Mellitus treatments. The model was developed in Scilab, which is a free software, in order to make it freely testable. According to measured error indexes, the new model fits better real clinical data and is more simple than others previously proposed.