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-i...
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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.
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 glo...
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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.
This paper describes a design of an educational platform for a mobile learning architecture, which is a state of the an topic in distance education. The product will allow users to interact in an efficient, flexible, ...
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This paper describes a design of an educational platform for a mobile learning architecture, which is a state of the an topic in distance education. The product will allow users to interact in an efficient, flexible, and transparent fashion with a web-based education environment, in this case Module Object-Oriented Dynamic Learning Environment （Moodle）, using Android mobile devices. In order to provide a strong and lasting architecture, the Service Oriented Architecture （SOA） methodology is used given that it allows easy software re-utilization as well as integration of heterogeneous services. The architecture is based on web services implemented with Representational State Transfer （REST）, as it has been demonstrated to be lighter and less consuming than other protocols, for devices with limited resources such as mobile devices. Web services provide the communication means between the server side and the client side of the architecture, whereas agents are used to deliver the services itself. The authors propose the development of an environment that facilitates the integration of various educational resources to support m-learning. An important aspect of the proposal is the offering of a tool to provide customized alerts for students and teachers, enabling them to remain updated about activities taking place in the courses.
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...
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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.
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 interfac...
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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.
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 call...
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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 litera...
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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 duri...
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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.
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 ...
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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 beh...
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