Application of domain evolution model-based genetic algorithm with fuzzy environment factor to system optimization

Genetic algorithms are able to search very large, variable complex spaces and locate the global optimum. However, there exist many difficulties in applying GA to large-scale nonlinear system optimization or “GA hard” problems. This paper presents an improved GA based on domain evolution model and fuzzy environment factor. Simulation study shows that it is a powerful search technique which can avoid premature convergence and locate the real global optimum. An example is given to show how this new algorithm can be successfully applied to solve large-scale industrial chemical separation process optimization problem