Leonardo D. Chiwiacowsky

Different approaches for the solution of a backward heat conduction problem

This work presents a comparison of three different techniques to solve the inverse heat conduction problem involving the estimation of the unknown initial condition for a one-dimensional slab, whose solution is obtained through minimization of a known functional form. The following techniques are employed to solve the inverse problem: the conjugate gradient method with the adjoint equation, regularized solution using a quasi-Newton method, and regularized solution via genetic algorithm (GA) method. For the first one, a general form to compute the gradient of the functional form (considering the time and space domains) is presented, and for the GA method a new genetic operator named epidemical is applied.

A variational approach for solving an inverse vibration problem

The present investigation is focused on the solution of a dynamic inverse problem which is concerned with the assessment of damage in structures by means of measured vibration data. This inverse problem has been presented as an optimization problem and has been solved through the use of the Variational Approach, i.e. the conjugate gradient method (CGM) coupled with the adjoint equation. The results have been presented in a satisfactory form when a small structure with few degrees of freedom (DOF) is considered, however, when a higher DOF structure is studied, the simple application of the variational approach is not any more satisfactory, being necessary the application of an additional methodology. In order to solve this difficulty, a new approach based on the use of the genetic algorithm (GA) method has been proposed. The GA method is used to generate a primary solution which is employed as the initial guess for the CGM. The application of this new approach has shown that better results can be achieved, although the computational time for the application analyzed here could be increased. The damage estimation has been evaluated using noiseless and noisy synthetic experimental data, and the reported results are concerned with both truss and beam-like structures, which have been modeled through a finite element technique. Moreover, in order to take into account the reduced set of experimental data to be employed in the optimization algorithm, a Guyan reduction technique has been adopted on the finite element formulation. ¶Selected paper from Inverse Problems, Design and Optimization Symposium, 2004.

Variations of Ant Colony Optimization for the Solution of the Structural Damage Identification Problem

In this work the inverse problem of identification of structural stiffness coefficients of a damped spring-mass system is tackled. The problem is solved by using different versions of Ant Colony Optimization (ACO) metaheuristic solely or coupled with the Hooke-Jeeves (HJ) local search algorithm. The evaluated versions of ACO are based on a discretization procedure to deal with the continuous domain design variables together with different pheromone evaporation and deposit strategies and also on the frequency of calling the local search algorithm. The damage estimation is evaluated using noiseless and noisy synthetic experimental data assuming a damage configuration throughout the structure. The reported results show the hybrid method as the best choice when both rank-based pheromone deposit and a new heuristic information based on the search history are used.

A comparison of two different approaches for the damage identification problem

This work deals with a structural damage detection problem using displacement measurements as experimental data to be used by two different methodologies: the conjugate gradient method with the adjoint equation and an artificial neural network. Both techniques have been employed in order to place and quantify the time-variable damage in a simple truss structure. Numerical experiments have been carried out with synthetic experimental data considering a noise level of 1%. Good recoveries have been achieved with both methodologies and estimation errors have been reported in order to make their comparison possible.

Combining a Parallel Genetic Algorithm with Variational Approach for Assessing Structural Damage

Considerable research and effort over the last few decades has taken place in the field of system identi- fication problem for different reasons. One of the most interesting applications involves the monitoring of structural integrity through the identification of damage. The basic idea remains that measured modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties, such as reductions in stiffness resulting from the onset of cracks, loosening of a connection or more in general due to the aging of material, will cause detectable changes in these modal properties.

Multi-Particle Collision Algorithm with Hooke Jeeves applied to the damage identification in a Kabe Problem

A new variant of the hybrid metaheutic MPCAHJ (Multi-Particle Collision Algorithm with Hooke-Jeeves method) is presented. Multi-Particle Collision Algorithm is a metaheuristic algorithm that performs a search on the search space. With the addition of the Rotation-Based Learning mechanism to the exploration search, a maior area of the search space has chance to be visited. The Hooke-Jeeves direct search method exploites the best solution found, allowing to achieve better solutions. The performance of all implementation are evaluated over twenty-two well known benchmark functions.

Cell formation problem: An multithreading Tabu search for setup time optimization for limited machine magazines: A New Solution For A Classical Problem

This paper aims to present a solution for the Manufacturing Cell Formation Problem through the use of a multithreading Tabu Search that uses deterministic methods to effectively explore local optimum areas. Manufacturing Cell Formation problem involves the process of analysing parts and groups them according to their similarity. This paper aims maximize the production efficiency, by minimizing the machine setup time in a limited magazine size through the reduction of tool changes by creating clusters of parts that share machining tools and present an initial scheduling based on tool changes reduction. In order to valid the proposed algorithm, the results obtained are compared against other Tabu Search solutions proposed in the literature.

A hybrid metaheuristic approach to optimize the content transmission in multimedia systems

The advent of the digital television in Brazil has allowed users to access interactive channels. Once interactive channels are available, the users are able to find multimedia content such as movies and breaking news programs, to send and/or receive emails, to access interactive applications and also other contents. In this context, a high demand of requests from users is expected. Therefore, from the content providers point of view, the determination of transmission parameters is needed in order to ensure the best quality of transmission to every user. The aforementioned identification problem is modelled as an optimization problem and a solution procedure based on metaheuristic techniques is proposed. Genetic Algorithm and Tabu Search metaheuristics are employed separately and coupled in a hybrid scheme to define the best transmission policy, optimizing the transmission parameters, such as audio and video transmission rates. Based on the experimental results, the hybrid algorithm has produced better solutions which meet the quality requirements.

A novel framework for supporting the exponential worldwide adoption of electronic transactions

Electronic transactions have become the mainstream mechanism for performing commerce activities in our daily lives. Aiming at processing them, the most common approach addresses the use of a switch that dispatches transactions to processing machines using the so-called Round-Robin scheduler. Considering this electronic funds transfer (EFT) scenario, we developed a framework model denoted GetLB which comprises not only a new and efficient scheduler, but also a cooperative communication infrastructure for handling heterogeneous and dynamic environments. The GetLB scheduler uses a scheduling heuristic that combines static data from transactions and dynamic information from the processing nodes to overcome the limitations of the Round-Robin based schedulinperiodic interactiong approaches. Scheduling efficiency takes place thanks to the periodic interaction between the switching node and processing machines, enabling local decision making with up-to-date information about the environment. Besides the description of the aforementioned model in detail, this article also presents a prototype evaluation by using both traces and configurations obtained with a real EFT company. The results show improvements in transaction makespan when comparing our approach with the traditional one over homogeneous and heterogeneous clusters.

A two-level procedure based on genetic algorithms to optimize an aeronautical composite structure

Optimal design of complex engineering systems, such as aircraft composite structures, can often be accomplished only by applying decomposition techniques. In this paper, the optimal design of a composite wing-box is addressed by using a two-level scheme based on the Genetic Algorithm meta-heuristic. The two-level optimization strategy was evaluated for variations both in the angular step of the composite layers and in the strength of material.