Mirosław Szczepanik

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.

Topological evolutionary computing in the optimal design of 2D and 3D structures

An application of evolutionary algorithms and the finite-element method to the topology optimization of 2D structures (plane stress, bending plates, and shells) and 3D structures is described. The basis of the topological evolutionary optimization is the direct control of the density material distribution (or thickness for 2D structures) by the evolutionary algorithm. The structures are optimized for stress, mass, and compliance criteria. The numerical examples demonstrate that this method is an effective technique for solving problems in computer-aided optimal design. †This is an extended and enhanced version of work presented at the mini−symposium on Evolutionary Algorithms: Recent Applications in Engineering and Science organized by Dr William Annicchiarico at the 7th World Congress on Computational Mechanics, Los Angeles, July 2006.

Immune Computing: Intelligent Methodology and Its Applications in Bioengineering and Computational Mechanics

The aim of this paper is to provide a set of carefully selected problems connected with the current research directions of Immune Computing. This approach belongs to biology inspired methods. Due to the complexity of functioning of the natural immune system, extracting higher level paradigms which could serve as the basis of constructing computational models and algorithmic solutions is made. Applications of this intelligent methodology to bioengineering and computational mechanics problems are presented.

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.

Hybrid particle swarm optimizer and its application in identification of room acoustic properties

The paper deals with an application of an hybrid particle swarm optimizer (HPSO) to identification problems. The HPSO is applied to identify complex impedances of room walls and it is based on the mechanism discovered in the nature during observations of the animals social behaviour and supplemented with some additional gradient information. The numerical example demonstrate that the method based on hybrid swarm optimization is an effective technique for computing in identification problems.

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.

Swarm and Immune Computing of Dynamically Loaded Reinforced Structures

In the paper an application of the particle swarm optimizer (PSO) and artificial immune system (AIS) to optimization problems is presented. Reinfored structures considered in this work are dynamically loaded and analyzed by the coupled boundary and finite element method (BEM/FEM). The metod is applied to optimize location of stiffeners in plates using criteria depended on displacements. The main advantage of the particle swarm optimizer, contrary to gradient methods of optimization, is the fact that it does not need any information about the gradient of fitness function. A comparison of the PSO, artificial immune system and evolutionary algorithm (EA) is also shown and it proves the efficiency of the former over other artificial intelligence methods of optimization. The coupled BEM/FEM, which is used to analyse structures, is very accu-rate in analysis and attractive in optimization tasks. It is because of problem dimensionality reduction in comparison with more frequently used domain methods, like for instance the FEM. Numerical examples demonstrate that the combination of the PSO with the BEM/FEM is an effective technique for solving computer aided optimal design problems, both with respect to accuracy and computational resources.

Genetic generation of 2D and 3D structures

Publisher Summary This chapter presents a paper that describes the application of evolutionary methods and finite element methods to a genetic generation of 2D and 3D structures. The shape, topology, material, or thickness of a structure are generated for optimization criteria of minimum stresses and volume. The numerical examples demonstrate that the genetic generation based on evolutionary computation is an effective tool of artificial intelligence for solving computer-aided optimal designs. Evolutionary methods have found various applications in mechanics, especially in structural optimization. The main features of these methods include simulation of biological processes based on heredity principles and natural selection to create optimal solutions represented by single chromosomes. Evolutionary models of computation can be performed by genetic algorithms. Evolutionary computations are performed on a population of individuals. The important feature of this approach is its great flexibility for 2D and 3D problems and the strong probability of finding global optimal solutions.