On dedicated evolutionary algorithms for large non-linear constrained optimization problems
Janusz Orkisz, Maciej Głowacki · 2014
This paper considers advances in development of dedicated Evolutionary Algorithms (EA) for efficiently solving large, non-linear, constrained optimization problems. The EA are precisely understood here as decimal-coded Genetic Algorithms consisting of three operators: selection, crossover and mutation, followed by several newly developed calculation speed-up techniques based on simple concepts. These techniques include: solution smoothing and balancing, a--posteriori solution error analysis and related techniques, non-standard use of distributed and parallel calculations, and adaptive step-by-step mesh refinement. Efficiency of the techniques proposed here has been evaluated using several benchmark problems e.g. residual stresses analysis in chosen elastic-plastic bodies under cyclic loadings. These preliminary tests indicate significant acceleration of the large optimization processes involved. The final objective of our research is development of an algorithm efficient enough for solving real, large engineering problems.