Wacław Kuś

Optimization and defect identification using distributed evolutionary algorithms

Abstract The aim of the paper is to present the application of the distributed evolutionary algorithms to selected optimization and defect identification problems. The coupling of evolutionary algorithms with the finite element method and the boundary element method creates a computational intelligence technique that is very suitable in computer aided optimal design. Several numerical examples for shape, topology optimization and identification are presented for elastic, thermoelastic and elastoplastic structures.

Sequential and Distributed Evolutionary Computations in Structural Optimization

The aim of the paper is to present the application of the sequential and distributed evolutionary algorithms to selected structural optimization problems. The coupling of evolutionary algorithms with the finite element method and the boundary element method creates a computational intelligence technique that is very suitable in computer aided optimal design. Several numerical examples for shape, topology and material optimization are presented.

Immune identification of piezoelectric material constants using BEM

This article deals with an application of the artificial immune system (AIS) to the identification problem of piezoelectric structures analysed by the boundary element method (BEM). The AIS is applied to identify material properties of piezoelectrics. The AIS is a computational adaptive system inspired by the principles, processes and mechanisms of biological immune systems. The algorithms typically use the characteristics of immune systems, such as learning and memory to simulate and solve a problem in a computational manner. The main advantage of the AIS, contrary to gradient methods of optimization, is the fact that it does not need any information about the gradient of fitness function.

Parallel bioinspired algorithms in optimization of structures

The parallel versions of bioinspired algorithms are presented in the paper. The parallel evolutionary algorithms and artificial immune systems are described. The applications of bioinspired algorithms to optimization of mechanical structures are shown. The numerical tests presented in the paper were computed with use of grid based on Alchemi framework.

Hybrid artificial immune system in identification of room acoustic properties

The paper deals with an application of a hybrid artificial immune system (HAIS) to the identification problems. The HAIS is applied to identify complex impedances of room walls. This approach is based on the mechanism discovered in biological immune systems. The numerical example demonstrates that the method based on immune computation is an effective technique for solving computer aided in identification problem.

Comparison between PSO and AIS on the Basis of Identification of Material Constants in Piezoelectrics

The paper deals with an application of the artificial immune system (AIS) and particle swarm optimizer (PSO) to the identification problem of piezoelectric structures analyzed by the boundary element method (BEM). The AIS and PSO is applied to identify material properties of piezoelectrics. The AIS is a computational adaptive system inspired by the principles, processes and mechanisms of biological immune systems. The algorithms typically use the characteristics of the immune systems like learning and memory to simulate and solve a problem in a computational manner. The PSO algorithm is based on the models of the animals social behaviours: moving and living in the groups. PSO algorithm realizes directed motion of the particles in n-dimensional space to search for solution for n-variable optimisation problem.The main advantage of the bioinspired methods (AIS and PSO), contrary to gradient methods of optimization, is the fact that it does not need any information about the gradient of fitness function.

Bio-inspired Optimization of Thermomechanical Structures

The paper is devoted to an application of particle swarm optimizer and artificial immune system to optimization of elastic bodies under thermomechanical loading. The optimization problem is formulated as minimization of the volume, the maximal value of the equivalent stress, the maximal value of the temperature or maximization of the total dissipated heat flux with respect to specific dimensions of a structure. The direct problem is computed by means of the finite element method. Numerical examples for shape optimization are also included.

Evolutionary Computation in Inverse Problems

Evolutionary computations in identification of multiple material defects (voids and cracks) in mechanical systems and identification of shape and position of a tumor region in the biological tissue domain are presented. The identification belongs to inverse problems and is treated here as an output (measurement) error minimization, which is solved using numerical optimization methods. The output error is defined in the form of a functional of boundary displacements or temperature fields. An evolutionary algorithm is employed to minimize of the functional. Numerical tests of internal defects identification and some anomalies in the tissue are presented.

Bioinspired Algorithms in Multiscale Optimization

The paper is devoted to bioinspired optimization in multiscale problems. The composite modeled as a macrostructure with a local periodic microstructure is considered. The multiscale analysis is performed with the use of the homogenization method. The evolutionary algorithm, the artificial immune system and the particle swarm optimization are used in computations. The objective function evaluation with the use of the parallel homogenization algorithm is considered. The paper contains a description of the evolutionary algorithm, artificial immune system, particle swarm optimization, the homogenization method, the optimization formulation.

Grid-Based evolutionary optimization of structures

The paper is devoted to computational grids applications in evolutionary optimization of mechanical structures. The LCG2 and UNICORE grid middleware are used. The optimization is performed by means of the distributed evolutionary algorithm. The fitness function is computed using the finite element method. The numerical example is presented in the paper.