ICCM Conferences, The 8th International Conference on Computational Methods (ICCM2017)

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On Improving Evolutionary Algorithms and Acceleration Techniques Based on Estimation of Convergence Point Population for Chosen Optimization Problems of Mechanics
Janusz Orkisz, Maciej Glowacki

Last modified: 2017-06-30


Several issues regarding development of highly accelerated and efficient Evolutionary Algorithms (EA) for solving large, non-linear, constrained optimization problems are considered in this work. In particular, we briefly present here advances in development of already proposed acceleration techniques, including smoothing and balancing, adaptive step-by-step mesh refinement, as well as a’posteriori error analysis and related techniques. Our most recent research has been focused mainly on searching of efficient combination of the proposed techniques and their parameters, as well as on development of some new concepts based on estimation of the convergence point of population. The improved EA-based approach provides significant speed-up of solution process and/or possibility of solving such large problems, when the standard EA methods fail.


Evolutionary Algorithms; acceleration techniques; large non-linear constrained optimization problems; convergence point of population

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