Modified cultural-based genetic algorithm for process optimization

The main weak points in using AI optimization technique are the possibility of being trapped at local minima, being confined to the population space, difficulty to solve heavily nonlinear problems and to make full use of the historical information beside the lack of prediction about the search space. In this paper, a hybrid optimization technique; namely culture-based genetic algorithm is proposed and tested against three multidimensional and highly nonlinear real world applications. This method proved to overcome most of these problems and the results showed that the proposed algorithm gives excellent performance for pressure vessel design and fed-batch fermentor problems.