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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

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.

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

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Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones

Improved quantum-inspired evolutionary algorithm with divers...

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作者： Neto, Júlio Xavier Vianna De Andrade Bernert, Diego Luis Dos Santos Coelho, Leandro Pontifical Catholic University of Parana PUCPR Undergraduate Program at Mechatronics Engineering Imaculada Conceição 1155 Zip code 80215-901 Curitiba Parana Brazil Pontifical Catholic University of Parana PUCPR Industrial and Systems Engineering Graduate Program LAS/PPGEPS Imaculada Conceição 1155 80215-901 Curitiba Parana Brazil

The objective of the economic dispatch problem (EDP) of electric power generation, whose characteristics are complex and highly nonlinear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying all unit and system equality and inequality constraints. Recently, as an alternative to the conventional mathematical approaches, modern meta-heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. Research on merging evolutionary computation and quantum computation has been started since late 1990. Inspired on the quantum computation, this paper presented an improved quantum-inspired evolutionary algorithm (IQEA) based on diversity information of population. A classical quantum-inspired evolutionary algorithm (QEA) and the IQEA were implemented and validated for a benchmark of EDP with 15 thermal generators with prohibited operating zones. From the results for the benchmark problem, it is observed that the proposed IQEA approach provides promising results when compared to various methods available in the literature. © 2010 Elsevier Ltd. All rights reserved.

关键词： 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： 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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A normative differential evolution approach for estimation of heat transfer coefficient during freezing treatment by inverse analysis

A normative differential evolution approach for estimation o...

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IEEE Congress on Evolutionary Computation

作者： Mariani, Viviana Cocco Klein, Carlos Eduardo Luvizotto, Luiz Guilherme Justi Dos Santos Coelho, Leandro Department of Mechanical Engineering PPGEM Pontifical Catholic University of Paran 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)9781424478347

Among the existing meta-heuristic optimization algorithms, a well-known branch is the differential evolution (DE). DE is a powerful population-based algorithm of evolutionary computation field designed for solving global optimization problems which only has a few control parameters. With an eye to improve the performance of DE, in this paper, a DE approach combined with a cultural algorithm technique based on normative knowledge (NDE) is investigated to estimate the heat transfer coefficient during freezing treatment by inverse analysis. Numerical results for inverse heat transfer problem demonstrate the applicability and efficiency of the NDE algorithm. In this application, NDE approach outperforms a classical DE approach in terms of quality of solution. © 2011 IEEE.

关键词： Inverse problems

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A chaotic firefly algorithm applied to reliability-redundancy optimization

A chaotic firefly algorithm applied to reliability-redundanc...

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IEEE Congress on Evolutionary Computation

作者： Dos Santos Coelho, Leandro De Andrade Bernert, Diego Luis Mariani, Viviana Cocco Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paran Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil Department of Mechanical Engineering PPGEM Pontifical Catholic University of Paran Imaculada Conceição 1155 80215-901 Curitiba Paran Brazil

ISBN: (纸本)9781424478347

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer nonlinear programming. On the other hand, a broad class of meta-heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called firefly algorithm (FA) algorithm has emerged. The FA is a stochastic metaheuristic approach based on the idealized behavior of the flashing characteristics of fireflies. In FA, the flashing light can be formulated in such a way that it is associated with the objective function to be optimized, which makes it possible to formulate the firefly algorithm. This paper introduces a modified FA approach combined with chaotic sequences (FAC) applied to reliability-redundancy optimization. In this context, an example of mixed integer programming in reliability-redundancy design of an overspeed protection system for a gas turbine is evaluated. In this application domain, FAC was found to outperform the previously best-known solutions available. © 2011 IEEE.

关键词： Integer programming

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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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A chaotic firefly algorithm applied to reliability-redundancy optimization

A chaotic firefly algorithm applied to reliability-redundanc...

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Congress on Evolutionary Computation

作者： Leandro dos Santos Coelho Diego Luís de Andrade Bernert Viviana Cocco Mariani Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil Department of Mechanical Engineering PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer non linear programming. On the other hand, a broad class of meta heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called firefly algorithm (FA) algorithm has emerged. The FA is a stochastic metaheuristic approach based on the idealized behavior of the flashing characteristics of fireflies. In FA, the flashing light can be formulated in such a way that it is associated with the objective function to be optimized, which makes it possible to formulate the firefly algorithm. This paper introduces a modified FA approach combined with chaotic sequences (FAC) applied to reliability-redundancy optimization. In this context, an example of mixed integer programming in reliability-redundancy design of an overspeed protection system for a gas turbine is evaluated. In this application domain, FAC was found to outperform the previously best-known solutions available.

关键词： Fires Optimization Chaos Algorithm design and analysis Benchmark testing Redundancy

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A normative differential evolution approach for estimation of heat transfer coefficient during freezing treatment by inverse analysis

A normative differential evolution approach for estimation o...

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Congress on Evolutionary Computation

作者： Viviana Cocco Mariani Carlos Eduardo Klein Luiz Guilherme Justi Luvizotto Leandro dos Santos Coelho Department of Mechanical Engineering PPGEM Pontifical Catholic University of Paraná Curitiba Parana Brazil Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil

关键词： Heat transfer Optimization Temperature measurement Cultural differences Algorithm design and analysis Evolutionary computation Temperature

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A combined deterministic and sampling-based sequential bounding method for stochastic programming 11

A combined deterministic and sampling-based sequential bound...

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Simulation Winter Conference

作者： Péguy Pierre-Louis Güzin Bayraksan David P. Morton Department of Systems and Industrial Engineering University of Arizona Tucson Tucson AZ USA Graduate Program in Operations Research University of Texas Austin Austin TX USA

We develop an algorithm for two-stage stochastic programming with a convex second stage program and with uncertainty in the right-hand side. The algorithm draws on techniques from bounding and approximation methods as well as sampling-based approaches. In particular, we sequentially refine a partition of the support of the random vector and, through Jensen's inequality, generate deterministically valid lower bounds on the optimal objective function value. An upper bound estimator is formed through a stratified Monte Carlo sampling procedure that includes the use of a control variate variance reduction scheme. The algorithm lends itself to a stopping rule theory that ensures an asymptotically valid confidence interval for the quality of the proposed solution. Computational results illustrate our approach.

关键词： Approximation algorithms Partitioning algorithms Monte Carlo methods Upper bound Linear approximation Vectors

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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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