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Congress on Evolutionary Computation

作者： Daniel M. Muñoz Carlos H. Llanos Leandro dos S. Coelho Mauricio Ayala-Rincón Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia Brazil Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia Brazil

Synthesis of musical instruments or human voice is a time consuming process which requires theoretical and experimental knowledge about the synthesis engine. Commonly, performers need to deal with synthesizer interfaces and a process of trial and error for creating musical sounds similar to a target sound. This drawback can be overcome by adjusting automatically the synthesizer parameters using optimization algorithms. In this paper a hybrid particle swarm optimization (PSO) algorithm is proposed to solve the frequency modulation (FM) matching synthesis problem. The proposed algorithm takes advantage of a shuffle process for exchanging information between particles and applies the selective passive congregation and the opposition-based learning approaches to preserve swarm diversity. Both approaches for injecting diversity are based on simple operators, preserving the easy implementation philosophy of the particle swarm optimization. The proposed hybrid particle swarm optimization algorithm was validated for a three-nested FM synthesizer, which represents a 6-dimensional multimodal optimization problem with strong epistasis. Simulation results revealed that the proposed algorithm presented promising results in terms of quality of solutions.

关键词： Frequency modulation Optimization Equations Convergence Particle swarm optimization Genetic algorithms

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Generating multiple time series forecasts with artificial neural networks in a telecommunications company

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International Journal of industrial engineering : Theory Applications and Practice 2011年第11期18卷 591-598页

作者： Salazar-Aguilar, María A. Villarreal-Marroquín, María G. Moreno-Rodríguez, Guillermo J. Rodriguez-Sarasty, Jesus A. Cabrera-Ríos, Mauricio Graduate Program in Systems Engineering Universidad Autónoma de Nuevo León San Nicolás de los Garza Nuevo León 66450 Mexico Department of Integrated Systems Engineering Ohio State University Columbus OH 43210 United States AXTEL-México San Pedro Garza García Nuevo León 66215 Mexico Department of Industrial Engineering University of Puerto Rico Mayagüez 00681 Puerto Rico

In this work, the generation of multiple period demand forecasts was approached through the use of artificial neural networks (ANNs) for time series. The study was carried out for a telecommunications company and called for the use of a method to fine-tune the ANNs by using design of experiments (DOE) and optimization techniques. The results give evidence of better forecasting performance of the ANNs over traditional statistical techniques. © International Journal of industrial engineering.

关键词： Neural networks

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Bayesian credit scoring model with integration of expert knowledge and customer data

Bayesian credit scoring model with integration of expert kno...

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24th Mini EURO Conference on Continuous Optimization and Information-Based Technologies in the Financial Sector, MEC EurOPT 2010

作者： Biçer, Işik Seviş, Deniz Bilgiç, Taner Department of Industrial Engineering Bogaziçi University Turkey Graduate Program of Systems and Control Engineering Bogaziçi University Turkey

ISBN: (纸本)9789955285977

Bayesian classification method is one of the effective classification methods in credit scoring applications. Application of this method to credit scoring provides several advantages, which are suggested in the literature. In this research, a Bayesian credit scoring model is applied for classification of personal loan customers. The contribution of this paper is two-fold. First, we apply a Bayesian classification model to credit scoring of personal loan customers with integration of knowledge of credit experts and personal loan data. The dependency relations between the variables are constructed based on the knowledge of credit experts, and the parameters are estimated by Expectation-Maximization (EM) algorithm with real personal loan data of customers obtained from a Turkish bank. Second, we assess the effectiveness of the Bayesian credit scoring model based on additional personal loan data of the bank customers. The results show that the Bayesian credit scoring model is more effective than the logistic regression credit scoring model which has been currently used in the bank. © Izmir University of Economics, Turkey, 2010.

关键词： Sales

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The use of central composite designs to improve cytotoxicity data generation: a case study

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IIE Transactions on Healthcare systems engineering 2011年第4期1卷 226-231页

作者： Isaza, Clara E. Rodriguez, Cristina Uribe, Lyzett Perez, Hugo A. Salinas, Jannet Cabrera-Rios, Mauricio Immunology and Virology Laboratory Universidad Autonoma de Nuevo Leon San NICOLAS DE LOS GARZA Av. Universidad S/N Mexico Graduate Program in Systems Engineering Universidad Autonoma de Nuevo Leon SAN NICOLAS DE LOS GARZA Av. Universidad S/N Mexico Industrial Engineering University of Puerto Rico-Mayagüez Mayaguez 00681 PO Box 9000 Puerto Rico

Research to find new treatments for cancer generally begins with in-vitro tests to measure the toxic effect of a proposed medical treatment on cell line cultures. In general, these tests are called cytotoxicity assays. The development of experimental protocols to conduct these tests is an attractive area for the application of experimental design techniques. This work describes the improvement of an experimental protocol for the colorimetric MTT assay, one of the most common cytotoxicity tests. The MTT assay is used in this case study to characterize the cytotoxic level of a novel immunotherapy agent on the well-known HeLa cervical cancer cell line. The original protocol related cytotoxicity to treatment concentration, treatment time and initial number of cells in the test. With the application of an experimental strategy based on the central composite design, it was possible to improve the protocol to drastically reduce the consumption of reagents and the use of laboratory time while strengthening the test conclusions. © 2011 Copyright Taylor and Francis Group, LLC.

关键词： biological experiments Decision analysis design of experiments linear regression optimization

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Counting vehicles: A learning experience

Counting vehicles: A learning experience

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IIE Annual Conference and Expo 2010

作者： Cabrera-Ríos, Mauricio Maldonado Chan, Mauro J. Industrial Engineering Dept University of Puerto Rico at Mayaguez Puerto Rico Graduate Program in Systems Engineering UANL San-Nicolás-de-los-Garza N.L. Mexico

Traffic flow characterization is critical for transportation systems and urban infrastructure planning. In most major cities in Latin America, this characterization is carried out visually by a group of observers during one-hour sampling periods. Automating this process should result in higher consistency and reliability in the output statistics, as well as helping keep the people in charge of this process safe. In this project, an automatic vehicle counting and classification system prototype is presented. The system uses video images previously recorded thru a camcorder and, upon an initial setup session, proves capable of detecting, counting and classifying the passing vehicles to an acceptable error rate. The preliminary evaluation is promising and provides a solid base for the development of the complete system.

关键词： Neural networks

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Hardware particle swarm optimization based on the attractive-repulsive scheme for embedded applications

Hardware particle swarm optimization based on the attractive...

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2010 International Conference on Reconfigurable computing and FPGAs, ReConFig 2010

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos S. Coelho, Leandro Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia DF Brazil Industrial and Systems Engineering Graduate Program LAS/PPGPES Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia DF Brazil

ISBN: (纸本)9780769543147

Particle Swarm Optimization (PSO) algorithms have been proposed to solve engineering problems that require to find an optimal point of operation. However, the PSO algorithm suffers from premature convergence and high elapsed time when solving multimodal and large scale engineering problems. This problem becomes an evident drawback for embedded applications in which the microcontrollers often operates at low computational capacity. This paper proposes a hardware implementation of a parallel self-adaptive PSO algorithm based on an attractive-repulsive scheme and using the efficient floating-point arithmetic which performs computations with large dynamic range and high precision. The parallel capabilities of the PSO are exploited by implementing parallel particles directly in hardware in order to decrease the running time. In addition, the attractive-repulsive technique avoids the premature convergence problem by self-adapting the swarm behavior according to its diversity. Synthesis and simulation results for benchmark test problems were performed, demonstrating the correctness of the proposed architectures. Finally, an elapsed time comparison between the hardware and software implementations shows the suitableness of the proposed architecture for embedded applications. © 2010 IEEE.

关键词： Global optimization

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Cauchy particle swarm optimization with dynamic adaptation applied to inverse heat transfer problem

Cauchy particle swarm optimization with dynamic adaptation a...

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2010 IEEE International Conference on systems, Man and Cybernetics, SMC 2010

作者： Mariani, Viviana Cocco Neckel, Vagner Jorge Grebogi, Rafael Bartnik Dos Santos Coelho, Leandro Department of Mechanical Engineering PPGEM Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil

ISBN: (纸本)9781424465880

The particle swarm optimization (PSO) algorithm is a member of the wide category of swarm intelligence methods for solving global optimization problems. Its basic idea is the simulation of simplified animal social behaviors such as fish schooling and bird flocking. PSO algorithms are attracting attentions in recent years, due to their ability of keeping good balance between convergence and diversity maintenance. Several attempts have been made to improve the performance of the original PSO algorithm. In this paper, a modified version of the original PSO based on Cauchy distribution and dynamic adaptation of inertia factor, named modified PSO (MPSO), is proposed. to estimate the unknown variables of an inverse heat transfer problem. To validate the optimization performance of the proposed MPSO, an inverse heat transfer problem is illustrated and the algorithm has to estimate its unknown variables. The results testify that the MPSO can perform well in an inverse heat transfer problem. ©2010 IEEE.

关键词： Heat transfer

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Accelerating the Shuffled Frog Leaping algorithm by parallel implementations in FPGAs

Accelerating the Shuffled Frog Leaping algorithm by parallel...

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2010 IEEE 5th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2010

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos S. Coelho, Leandro Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACG University of Brasilia Brasilia D.F. Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781424464388

Meta-heuristics are efficient techniques for solving large scale optimization problems in which traditional mathematical techniques are impractical or provide sub-optimal solutions. The Shuffled Frog Leaping algorithm (SFLA) is a stochastic iterative method, bio-inspired on the memetic evolution of a group of frogs when seeking for food, which combines the social behavior-based of the particle swarm optimization technique (PSO) and the global information exchange of memetic algorithms. However, the SFLA algorithm suffers on large execution times, being this problem clearly evident when solving complex optimization problems for embedded applications. This drawback can be overcome by exploiting the parallel capabilities of the SFLA. This paper proposes a hardware parallel implementation of the SFLA algorithm (HPSFLA) using FPGAs (Field programmable gate Arrays) and the efficient floating-point arithmetic. The proposed architecture allows the SFLA to improve the functionality of the algorithm as well as to decrease the execution times by implementing parallel frogs and parallel memeplexes. Three well-known benchmark problems have been used to validate the implemented algorithm and simulation results demonstrate that the HPSFLA speeds-up by factors of 362, 727 and 211 a C-code implementation using an embedded microprocessor for the Sphere, Rastrigin and Rosenbrock benchmarks problems, respectively. Synthesis, simulation and execution time results demonstrate the effectiveness of the proposed HPSFLA architecture for embedded optimization systems. © 2010 IEEE.

关键词： Global optimization

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Comparison between two FPGA implementations of the particle swarm optimization algorithm for high-performance embedded applications

Comparison between two FPGA implementations of the particle ...

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2010 IEEE 5th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2010

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos S. Coelho, Leandro Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia D.F. Brazil Industrial and Systems Engineering Graduate Program LAS/PPGPES Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781424464388

Particle Swarm Optimization (PSO) algorithms have been proposed to solve engineering problems that require to find an optimal point of operation. There are several embedded applications which requires to solve online optimization problems with a high performance. However, the PSO suffers on large execution times, and this fact becomes evident when using Reduced Instruction Set Computer (RISC) microprocessors in which the operational frequencies are low in comparison with the high operational frequencies of traditional personal computers (PCs). This paper compares two hardware implementations of the parallel PSO algorithm using an efficient floating-point arithmetic which perform computations with large dynamic range and high precision. The full-parallel and the partially-parallel PSO architectures allow the parallel capabilities of the PSO to be exploited in order to decrease the running time. Three well-known benchmark test functions have been used to validate the hardware architectures and a comparison in terms of cost in logic area, quality of the solution and performance is reported. In addition, a comparison of the execution time between the hardware and two C-code software implementations, based on a Intel Core Duo at 1.6GHz and a embedded Microblaze microprocessor at 50MHz, are presented. © 2010 IEEE.

关键词： Global optimization

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Geometric modeling from the medical images in the CAD system to support prosthesis design

Geometric modeling from the medical images in the CAD system...

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International Conference on Computers & industrial engineering (CIE)

作者： Osiris Canciglieri M. Rudek Tiago Francesconi Teófilo Miguel de Souza Pontificia Universidade Catolica do Parana Curitiba PR BR Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná Curitiba Parana Brazil Department of Manufacturing Engineering of Pontifical Industrial and Systems Engineering Graduate Program (PPGEPS) Curitiba PR Brazil Department of Electrical Engineering São Paulo State University (UNESP) Guaratinguetá SP Brazil

This paper presents an individual designing prosthesis for surgical use and proposes a methodology for such design through mathematical extrapolation of data from digital images obtained via tomography of individual patient's bones. Individually tailored prosthesis designed to fit particular patient requirements as accurately as possible should result in more successful reconstruction, enable better planning before surgery and consequently fewer complications during surgery. Fast and accurate design and manufacture of personalized prosthesis for surgical use in bone replacement or reconstruction is potentially feasible through the application and integration of several different existing technologies, which are each at different stages of maturity. Initial case study experiments have been undertaken to validate the research concepts by making dimensional comparisons between a bone and a virtual model produced using the proposed methodology and a future research directions are discussed.

关键词： Solid modeling Design automation Bones Tomography Prosthetics Surgery Image reconstruction

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