Genetic/Quadratic Search Algorithm for Plant Economic Optimizations Using a Process Simulator

The Genetic/Quadratic Search Algorithm (GQSA) is a hybrid Genetic Algorithms (GA) for optimizing plant economics when a process simulator models the plant. By coupling a regular GA with an algorithm based upon a quadratic search, the required number of objective function evaluations for obtaining an acceptable solution decreases significantly in most cases. The GQSA combines advantages of GA and quadratic search techniques, e.g. determining a global optimum for a problem with a high probability for discontinuous as well as non-convex optimization problems while at the same time providing faster convergence than conventional GA.

The performance of both the GQSA and the GA was compared using four different test functions and an economic optimization problem for a turbo-expander process. Numerical test results indicate that the convergence of the GQSA is either better than or at least comparable to those of GA for all tests employing the same genetic parameters.


W.H. Jang, K.R. Hall, and J. Hahn. "Genetic/Quadratic Search Algorithm for Plant Economic Optimizations Using a Process Simulator"

Computers and Chemical Engineering 30, No. 2, pp. 285-294 (2005)