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Accelerating the artificial bee colony algorithm by hardware parallel implementations

Accelerating the artificial bee colony algorithm by hardware...

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2012 IEEE 3rd Latin American Symposium on Circuits and systems, LASCAS 2012

作者： Muñoz, Daniel M. Llanos, Carlos H. Coelho, Leandro Dos S. 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/PPGEPS Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781467312080

Artificial bee colony (ABC) is an optimization algorithm inspired on the intelligent behavior of honey bee swarms. It is suitable to be applied when mathematical techniques are impractical or provide suboptimal solutions. As a population-based algorithm, the ABC suffers on large execution times specifically for embedded optimization problems with computational limitations. For that we propose a hardware parallel architecture of the opposition-based ABC algorithm (HPOABC) that facilitates the implementation in Field programmable Gate Arrays (FPGAs). Numerical simulations using four well-known benchmark problems demonstrate that the opposition-based approach allows the algorithm to improve its functionality, preserving the swarm diversity. Additionally, synthesis results point outs that the HPOABC architecture is effectively mapped in hardware and is suitable for embedded applications. © 2012 IEEE.

关键词： Global optimization

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Accelerating the Artificial Bee Colony Algorithm by Hardware Parallel Implementations

Accelerating the Artificial Bee Colony Algorithm by Hardware...

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IEEE Latin American Symposium on Circuits and systems

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

ISBN: (纸本)9781467312073

关键词： Global optimization Swarm intelligence Artificial bee colony FPGAs Floating-point arithmetic

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Hardware Particle Swarm Optimization with passive congregation for embedded applications

Hardware Particle Swarm Optimization with passive congregati...

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Southern Conference on programmable Logic

作者： Muñoz, Daniel M. Llanos, Carlos H. Coelho, Leandro Dos S. 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 Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781424488483

Achieving high performance optimization algorithms for embedded applications can be very challenging, particularly when several requirements such as high accuracy computations, short elapsed time, area cost, low power consumption and portability must be accomplished. This paper proposes a hardware implementation of the Particle Swarm Optimization algorithm with passive congregation (HPPSOpc), which was developed using several floating-point arithmetic libraries. The passive congregation is a biological behavior which allows the swarm to preserve its integrity, balancing between global and local search. The HPPSOpc architecture was implemented on a Virtex5 FPGA device and validated using two multimodal benchmark problems. Synthesis, simulation and execution time results demonstrates that the passive congregation approach is a low cost solution for solving embedded optimization problems with a high performance. © 2011 IEEE.

关键词： Digital arithmetic

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Opposition-based shuffled PSO with passive congregation applied to FM matching synthesis

Opposition-based shuffled PSO with passive congregation appl...

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IEEE Congress on Evolutionary Computation

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

ISBN: (纸本)9781424478347

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. © 2011 IEEE.

关键词： Global optimization

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Hardware Particle Swarm Optimization with passive congregation for embedded applications

Hardware Particle Swarm Optimization with passive congregati...

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Southern Conference on programmable Logic

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 Brazil Industrial and Systems Engineering Graduate Program LAS/PPGPES Pontifical Catholic University of Paraná Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brazil

关键词： Hardware Computer architecture Software Field programmable gate arrays Benchmark testing Optimization Software algorithms

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Opposition-based shuffled PSO with passive congregation applied to FM matching synthesis

Opposition-based shuffled PSO with passive congregation appl...

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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

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

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Discovering software process and product quality criteria in software as a service

Discovering software process and product quality criteria in...

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11th International Conference on Product-Focused Software Process Improvement, PROFES 2010

作者： Cancian, Maiara Heil Hauck, Jean Carlo Rossa Von Wangenheim, Christiane Gresse Rabelo, Ricardo José Department of Automation and Systems Federal University of Santa Catarina Brazil Graduate Program in Knowledge Engineering and Management Federal University of Santa Catarina Brazil Federal University of Santa Catarina Brazil

ISBN: (纸本)3642137911

SaaS (Software as a Service) has become one of the fastest-growing innovative fields of the IT sector. Yet, as any other software intensive organizations, SaaS providers also need to deliver the service quality they offer. And, although, today exist a multitude of software quality models, so far, a specific adaptation does not exist to the SaaS context. Therefore, we present the results of our research on discovering software process and product quality criteria in the SaaS scenario. We adopted a research methodology, including, domain analysis, stakeholders interviews and literature review to elicit quality criteria and a survey to validate and prioritize the identified criteria. Such a set of identified quality criteria may help service clients to select providers as well as serve as a basis for a mapping relevant software process areas and best practices in order to adapt existing capability/maturity models and standards to this specific domain. © 2010 Springer-Verlag.

关键词： Quality control

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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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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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