On Several Acceleration Techniques for Evolutionary Algorithms Applied to Large Non-linear Constrained Optimization Problems

Abstract

This paper briefly presents advances in development of efficient Evolutionary Algorithms (EA) for a wide class of large non-linear constrained optimization problems. In particular, two important engineering applications are taken into account, namely residual stress analysis in railroad rails, and vehicle wheels, as well as a wide class of problems resulting from the Physically Based Approximation (PBA) of experimental data. However, the main objective of this research is to develop various means of significant acceleration of the EA-based approach for large optimization problems, and to provide ability to solve problems when standard EA procedure fails. The efficiency of speed-up techniques proposed was examined using several simple but demanding benchmark problems. Results obtained so far are very encouraging and indicate possibilities of further development of acceleration techniques proposed.