Laboratorio Nacional Of Informatica Avanzada Lania Ac

Veracruz, Mexico

Laboratorio Nacional Of Informatica Avanzada Lania Ac

Veracruz, Mexico
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Mezura-Montes E.,Laboratorio Nacional Of Informatica Avanzada Lania Ac
2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 | Year: 2010

A novel algorithm to solve constrained realparameter optimization problems, based on the Artificial Bee Colony algorithm is introduced in this paper. The operators used by the three types of bees (employed, onlooker and scout) are modified in such a way that more diverse and convenient solutions are generated. Furthermore, a dynamic tolerance control mechanism for equality constraints is added to the algorithm in order to facilitate the approach to the feasible region of the search space. Finally, two simple local search operators are applied to the best solution found so far. The algorithm, called Elitist-ABC, is tested on 18 test problems based on the experimental design proposed for the CEC'2010 competition on constrained real-parameter optimization. The results obtained are discussed and some conclusions are drawn. © 2010 IEEE.


Mezura-Montes E.,Laboratorio Nacional Of Informatica Avanzada Lania Ac | Portilla-Flores E.A.,Cidetec | Hernandez-Ocana B.,Laboratorio Nacional Of Informatica Avanzada Lania Ac
International Journal of Systems Science | Year: 2014

This paper presents the mechanical synthesis of a four-bar mechanism, its definition as a constrained optimisation problem in the presence of one dynamic constraint and its solution with a swarm intelligence algorithm based on the bacteria foraging process. The algorithm is adapted to solve the optimisation problem by adding a suitable constraint-handling technique that is able to incorporate a selection criterion for the two objectives stated by the kinematic analysis of the problem. Moreover, a diversity mechanism, coupled with the attractor operator used by bacteria, is designed to favour the exploration of the search space. Four experiments are designed to validate the proposed model and to test the performance of the algorithm regarding constraint-satisfaction, sub-optimal solutions obtained, performance metrics and an analysis of the solutions based on the simulation of the four-bar mechanism. The results are compared with those provided by four algorithms found in the specialised literature used to solve mechanical design problems. On the basis of the simulation analysis, the solutions obtained by the proposed algorithm lead to a more suitable design based on motion generation and operation quality. © 2012 Taylor & Francis.


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.


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.


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.


Gordian-Rivera L.-A.,Laboratorio Nacional Of Informatica Avanzada Lania Ac | Mezura-Montes E.,Laboratorio Nacional Of Informatica Avanzada Lania Ac
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

A novel approach based on three differential evolution variants to solve numerical constrained optimization problems is presented. Each variant competes to get more vectors for reproduction from the population. Such competition is based on two performance measures for convergence and solution improvement. Two of the variants adopted in this work were precisely proposed to deal with constrained search spaces. Two experiments are carried out: one to analyze the behavior of each variant with respect to the features of the problem solved and another to compare the performance of the proposed approach with respect to stateof- the-art multi-operator algorithms. The results obtained show that the specialized variants are more useful in the search, either combined or just using one of them. Finally, the final results of the proposed approach were highly competitive, and better in some cases, with respect to those of the algorithms used in the comparison. © Springer-Verlag Berlin Heidelberg 2012.


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.


Dominguez-Isidro S.,Laboratorio Nacional Of Informatica Avanzada Lania Ac | Mezura-Montes E.,Laboratorio Nacional Of Informatica Avanzada Lania Ac | Osorio-Hernandez L.G.,Laboratorio Nacional Of Informatica Avanzada Lania Ac
Genetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication | Year: 2011

This paper presents the use of an evolutionary metaheuristic algorithm called evolutionary programming to minimize the length of addition chains, which is an NP-hard problem. Addition chains are used in modular exponentiation for data encryption and decryption public-key cryptosystems, such as RSA, DSA and others. The algorithm starts with a population of feasible addition chains. After that, the combination of a mutation operator, which allows each individual to generate a feasible offspring, and a replacement process based on stochastic encounters provides a simple approach which is tested on exponents with different features. The proposed algorithm is able to find competitive results with respect to other nature-inspired metaheuristic approaches but with a lower number of evaluations per run. © 2011 Authors.


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.


Mezura-Montes E.,Laboratorio Nacional Of Informatica Avanzada Lania Ac | Damin-Araoz M.,Instituto Tecnologico De Orizaba | Cetina-Domingez O.,Laboratorio Nacional Of Informatica Avanzada Lania Ac
2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 | Year: 2010

This paper presents an adaptation of a novel algorithm based on the foraging behavior of honey bees to solve constrained numerical optimization problems. The modifications focus on improving the way the feasible region is approached by using a new operator which allows the generation of search directions biased by the best solution so far. Furthermore, two dynamic tolerances applied in the constraint handling mechanism help the algorithm to the generation of feasible solutions. The approach is tested on a set of 24 benchmark problems and its behavior is compared against the original algorithm and with respect to some state-of-the-art algorithms. © 2010 IEEE.

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