Design Optimization Integrating the Outer Approximation Method with Process Simulators and Linear Genetic Programming.
Larry M. Deschaine, Frank D. Francone · 2002
Fast process optimization is a challenge. Processes are often complex and the intricate simulators written to solve them can take hours or days per simulation to run. Optimization techniques that require many calls to a simulator can take days or months to solve. While advances in optimization algorithms, such as the outer approximation method have reduced the solution time by a factor of ten or more when compared to other methods, long solutions times still can occur. This work explores the development of simulating a simulator to enable optimal solution development in an accelerated time frame. The technique used to develop the simulated simulator is linear genetic programming (LGP). LGP approximated a complex industrial process simulator that took hours to execute per run with a high fitness program- applied (testing) data set R 2 fitness of 0.989. The LGP solution executes in less than a second. This success opens up the possibility of optimizing functions faster using these LGP derived high fitness simulator approximations. Since the LGP simulated process simulator now executes in less than a second, as opposed to hours, using an intensive multiple call optimization technique such as genetic algorithms and evolutionary strategies is now also feasible.