On Efficiency Increase of Evolutionary Algorithms for Large Non-linear Constrained Optimization Problems with Applications to Mechanics

Abstract

Advances in development of highly efficient dedicated Evolutionary Algorithms (EA) for a wide class of large non-linear constrained optimization problems are considered in this paper. The first objective of this general research is development and application of the improved EA to residual stress analysis in railroad rails and vehicle wheels. However, the standard EA are not sufficiently efficient for solving such large optimization problems. Therefore, our current research is mostly focused on development of various new very efficient acceleration techniques proposed, including smoothing and balancing, adaptive step-by-step mesh refinement, as well as a’posteriori error analysis and related techniques. This paper presents an efficiency analysis of chosen speed-up techniques using several simple but demanding benchmark problems, including residual stress analysis in elastic-plastic bodies under cyclic loadings. Preliminary results obtained for numerical tests are encouraging and show a clear possibility of practical application of the improved EA to large optimization problems.