Time filter

Source Type

Mezura-Montes E.,Laboratorio Nacional Of Informatica Avanzada Lania Ac | Lopez-Davila E.A.,Instituto Tecnologico De Orizaba
2012 IEEE Congress on Evolutionary Computation, CEC 2012 | Year: 2012

This paper presents the addition of an adaptive stepsize value and a local search operator to the modified bacterial foraging algorithm (MBFOA) to solve constrained optimization problems. The adaptive stepsize is used in the chemotactic loop for each bacterium to promote a suitable sampling of solutions and the local search operator aims to promote a better trade-off between exploration and exploitation during the search. Three MBFOA variants, the original one, another with only the adaptive stepsize and a third one with both, the adaptive stepsize and also the local search operator are tested on a set of well-known benchmark problems. Furthermore, the most competitive variant is compared against some representative nature-inspired algorithms of the state-of-the-art. The results obtained provide evidence on the utility of each added mechanism, while the overall performance of the approach makes it a viable option to solve constrained optimization problems. © 2012 IEEE. Source

Mezura-Montes E.,Laboratorio Nacional Of Informatica Avanzada Lania Ac | Coello Coello C.A.,CINVESTAV
Swarm and Evolutionary Computation | Year: 2011

In their original versions, nature-inspired search algorithms such as evolutionary algorithms and those based on swarm intelligence, lack a mechanism to deal with the constraints of a numerical optimization problem. Nowadays, however, there exists a considerable amount of research devoted to design techniques for handling constraints within a nature-inspired algorithm. This paper presents an analysis of the most relevant types of constraint-handling techniques that have been adopted with nature-inspired algorithms. From them, the most popular approaches are analyzed in more detail. For each of them, some representative instantiations are further discussed. In the last part of the paper, some of the future trends in the area, which have been only scarcely explored, are briefly discussed and then the conclusions of this paper are presented. © 2011 Elsevier B.V. All rights reserved. Source

Rechy-Ramirez F.,University of Veracruz | Mesa H.-G.A.,University of Veracruz | Mezura-Montes E.,Laboratorio Nacional Of Informatica Avanzada Lania Ac | Cruz-Ramirez N.,University of Veracruz
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

In this work, we present a novel algorithm for time series discretization. Our approach includes the optimization of the word size and the alphabet as one parameter. Using evolutionary programming, the search for a good discretization scheme is guided by a cost function which considers three criteria: the entropy regarding the classification, the complexity measured as the number of different strings needed to represent the complete data set, and the compression rate assessed as the length of the discrete representation. Our proposal is compared with some of the most representative algorithms found in the specialized literature, tested in a well-known benchmark of time series data sets. The statistical analysis of the classification accuracy shows that the overall performance of our algorithm is highly competitive. © 2011 Springer-Verlag. Source

Mezura-Montes E.,Laboratorio Nacional Of Informatica Avanzada Lania Ac | Miranda-Varela M.E.,Istmo University of Mexico | Del Carmen Gomez-Ramon R.,University Del Carmen
Information Sciences | Year: 2010

Motivated by the recent success of diverse approaches based on differential evolution (DE) to solve constrained numerical optimization problems, in this paper, the performance of this novel evolutionary algorithm is evaluated. Three experiments are designed to study the behavior of different DE variants on a set of benchmark problems by using different performance measures proposed in the specialized literature. The first experiment analyzes the behavior of four DE variants in 24 test functions considering dimensionality and the type of constraints of the problem. The second experiment presents a more in-depth analysis on two DE variants by varying two parameters (the scale factor F and the population size NP), which control the convergence of the algorithm. From the results obtained, a simple but competitive combination of two DE variants is proposed and compared against state-of-the-art DE-based algorithms for constrained optimization in the third experiment. The study in this paper shows (1) important information about the behavior of DE in constrained search spaces and (2) the role of this knowledge in the correct combination of variants, based on their capabilities, to generate simple but competitive approaches. © 2010 Elsevier B.V. All rights reserved. Source

Arias-Montano A.,CINVESTAV | Coello Coello C.A.,CINVESTAV | Mezura-Montes E.,Laboratorio Nacional Of Informatica Avanzada Lania Ac
Studies in Computational Intelligence | Year: 2011

Optimization problems in many industrial applications are very hard to solve. Many examples of them can be found in the design of aeronautical systems. In this field, the designer is frequently faced with the problem of considering not only a single design objective, but several of them, i.e., the designer needs to solve a Multi-Objective Optimization Problem (MOP). In aeronautical systems design, aerodynamics plays a key role in aircraft design, as well as in the design of propulsion system components, such as turbine engines. Thus, aerodynamic shape optimization is a crucial task, and has been extensively studied and developed. Multi-Objective Evolutionary Algorithms (MOEAs) have gained popularity in recent years as optimization methods in this area, mainly because of their simplicity, their ease of use and their suitability to be coupled to specialized numerical simulation tools. In this chapter, we will review some of the most relevant research on the use of MOEAs to solve multi-objective and/or multi-disciplinary aerodynamic shape optimization problems. In this review, we will highlight some of the benefits and drawbacks of the use of MOEAs, as compared to traditional design optimization methods. In the second part of the chapter, we will present a case study on the application of MOEAs for the solution of a multi-objective aerodynamic shape optimization problem. © 2011 Springer-Verlag Berlin Heidelberg. Source

Discover hidden collaborations