Improvement of the Optimal Design Procedure Using Randomized Algorithm and Process Simulators

Though a global optimization procedure using a randomized algorithm and a commercial process simulator is relatively easy to implement for complex design problems (i.e., intensified design processes), a dominant problem is their heavy computation load. As the process simulation is repeatedly executed to calculate the objective function, it is inevitable to spend long computation time to derive the optimal solution. Also, the randomized algorithms consider the treatment of all variables as continuous. Thus, the reduction of the number of iterations is crucial for such optimization procedures that include integer variables. In this work, an estimation procedure of the objective function having integer design variables is proposed. In the proposed procedure, the values of the objective function at the nodes of hyper-triangle that includes the suggested next search point are used to estimate the objective function, at the same time normalization of the design optimization variables is recommended. The procedure was implemented on the simulated annealing stochastic algorithm with a trivial case of a binary mixture in order to know the optimal solution and compare the traditional optimizations procedures and the proposed one. The proposed procedure show improvement not only for reducing the number iterations, but also for an increase of accuracy of finding the optimal solution