Differential Evolution performances for the solution of mixed-integer constrained process engineering problems

Authors:
A. Ponsich;C.A. Coello Coello
Affiliations:
CINVESTAV-IPN (Evolutionary Computation Group), Departamento de Computación, Av. Instituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, México, D.F. 07300, Mexico;CINVESTAV-IPN (Evolutionary Computation Group), Departamento de Computación, Av. Instituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, México, D.F. 07300, Mexico
Venue:
Applied Soft Computing
Year:
2011

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Abstract

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.