Differential Evolution performances for the solution of mixed-integer constrained process engineering problems
An important number of publications deal with the computational efficiency of a novel Evolutionary Algorithm called Differential Evolution (DE). However, there is still a noticeable lack of studies on DE's performance on engineering problems, which combine large-size instances, constraint-handling and mixed-integer variables issues. This paper proposes the solution by DE of process engineering problems and compares its computational performance with an exact optimization method (Branch-and-Bound) and with a Genetic Algorithm. Two analytical formulations are used to model the batch plant design problem and a set of examples gathering the three above-mentioned issues are also provided. The computational results obtained highlight the clear superiority of DE since its best found solutions always lie very close to the Branch-and-Bound optima. Moreover, for an equal number of objective function evaluations, the results repeatability was found to be much better for the DE method than for the Genetic Algorithm.
differential evolution process engineering mixed-integer constrained optimization