Adam Długosz

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.

Evolutionary Computation in Thermoelastic Problems

The paper deals with the application of evolutionary algorithms (EA) and the boundary element method (BEM) in optimization and identification of elastic structures under thermomechanical loading. A shape optimization problem was solved for various thermomechanical criteria with upper bound on the volume of the structure. The identification of voids basing on the information about measured displacements and temperatures in boundary sensor points was also considered. This problem was solved as well as for both ideal deterministic and randomly disturbed values of measured displacements and temperatures. Several tests and practical examples of optimization and identification were presented.

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.

Granular Computing in Evolutionary Identification

The paper deals with the application of the Two–Stage Granular Strategy (TSGS) to the identification problems. Identification of selected parameters of the structures is performed. The identification problem is formulated as the minimization of some objective functionals which depend on measured and computed fields. It is assumed that identified constants and measurements have non–deterministic character. Three forms of the information granularity are considered: interval numbers, fuzzy numbers and random variables. The strategy combines the following techniques: Evolutionary Algorithms (EAs), Artificial Neural Networks (ANNs), local optimization methods (LOMs) and Finite Element Method (FEM). All techniques are appropriately modified to deal with non–deterministic data. The EA is used in the first stage to perform the global optimization. The LOM supported by ANN is used in the second stage. The FEM computations are performed to solve the boundary–value problem. Numerical examples presenting the efficiency of the TSGS in different applications are attached.

Evolutionary BEM Computation in Shape Optimization Problems

The aim of the paper is to develop of the coupling of the boundary element method (BEM) and evolutionary algorithms (EA) to shape optimization problems in applied sciences and engineering. New approaches of the evolutionary BEM computation in optimization are proposed to: (i) shape optimization of structures under statical and dynamical loading, (ii) shape optimization of structures under thermomechanical loading, (iii) shape optimization of cracked structures for criteria expressed by stress intensity factors, and (iv) shape optimization of elasto-plastic structures. Several numerical examples for optimization of 2-D structures are presented.

The optimal design of UAV wing structure

The paper presents an optimal design of UAV wing, made of composite materials. The aim of the optimization is to improve strength and stiffness together with reduction of the weight of the structure. Three different types of functionals, which depend on stress, stiffness and the total mass are defined. The paper presents an application of the in-house implementation of the evolutionary multi-objective algorithm in optimization of the UAV wing structure. Values of the functionals are calculated on the basis of results obtained from numerical simulations. Numerical FEM model, consisting of different composite materials is created. Adequacy of the numerical model is verified by results obtained from the experiment, performed on a tensile testing machine. Examples of multi-objective optimization by means of Pareto-optimal set of solutions are presented.

Parallel evolutionary optimization of heat radiators by using MSC MARC/MENTAT software

The paper deals with the application of parallel evolutionary algorithms [1] (PEA) and the finite element method [3] (FEM) in shape optimization of heat radiators. The fitness function is computed by means of the thermoelsticity problem modeled by MSC MARC/MENTAT software. In order to create mesh, boundary conditions and material properties of the model a preprocessor MENTAT is used. Internal script language implemented in MENTAT allows avoiding external mesher procedure. Another benefit of this approach is that MENTAT takes into account shadowing effect in radiation [2]. Figure 1a shows the main steps of evaluation of the fitness function. The aim of the optimization is to find the optimal shape of the heat radiator shown in Figure 1b. This problem is solved by the minimization of the different types of functionals. Open image in new window Figure 1. a)Evaluation of the fitness function b) The geometry of the heat radiator