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On improved evolutionary algorithms application to the physically based approximation of experimental data

Orkisz J., Głowacki M.

Computer Assisted Methods in Engineering and Science

2014

Vol. 21, no. 1

27--38

In this paper an evolutionary algorithms (EA) application to the physically based approximation (PBA) of experimental and/or numerical data is considered. Such an approximation may simultaneously use the whole experimental, theoretical and heuristic knowledge about the analyzed problems. The PBA may be also applied for smoothing discrete data obtained from any rough numerical solution of the boundary value problem, and for solving inverse problems as well, like reconstruction of residual stresses based on experimental data. The PBA presents a very general approach formulated as a large non-linear constrained optimization problem. Its solution is usually complex and troublesome, especially in the case of non-convex problems. Here, considered is a solution approach of such problems based on the EA. However, the standard EA are rather slow methods, especially in the final stage of optimization process. In order to increase their solution efficiency, several acceleration techniques were introduced. Various benchmark problems were analyzed using the improved EA. The intended application of this research is reconstruction of residual stresses in railroads rails and vehicle wheels based on neutronography measurements.

Advances in development of dedicated evolutionary algorithms for large non-linear constrained optimization problems

Głowacki M., Orkisz J.

IPPT Reports on Fundamental Technological Research

2013

nr 4

25--29

Efficient optimization algorithms are of great importance in many scientific and engineering applications. This paper considers development of dedicated Evolutionary Algorithms (EA) based approach for solving large, non-linear, constrained optimization problems. The EA are precisely understood here as decimal-coded Genetic Algorithms consisting of three basic operators: selection, crossover and mutation, followed by several newly developed calculation speed-up techniques. Efficiency increase of the EA computations may be obtained in several ways, including simple concepts proposed here like: solution smoothing and balancing, a posteriori solution error analysis, non-standard use of distributed and parallel calculations, and step-by-step mesh refinement. Efficiency of the proposed techniques has been evaluated using several benchmark tests. These preliminary tests indicate significant speed-up of the large optimization processes involved. Considered are applications of the EA to the sample problem of residual stresses analysis in elastic-plastic bodies being under cyclic loadings, and to a wide class of problems resulting from the Physically Based Approximation (PBA) of experimental data.