Performance evaluation of local surrogate models in differential evolution-based optimum design of truss structures. Issue 2 (18th April 2017) - Krempser, Eduardo; Bernardino, Heder S.; Barbosa, Helio J.C.; Lemonge, Afonso C.C.
Abstract : Purpose: The purpose of this paper is to propose and analyze the use of local surrogate models to improve differential evolution's (DE) overall performance in computationally expensive problems. Design/methodology/approach: DE is a popular metaheuristic to solve optimization problems with several variants available in the literature. Here, the offspring are generated by means of different variants, and only the best one, according to the surrogate model, is evaluated by the simulator. The problem of weight minimization of truss structures is used to assess DE's performance when different metamodels are used. The surrogate-assisted DE techniques proposed here are also compared to common DE variants. Six different structural optimization problems are studied involving continuous as well as discrete sizing design variables. Findings: The use of a local, similarity-based, surrogate model improves the relative performance of DE for most test-problems, specially when using r-nearest neighbors with r = 0.001 and a DE parameter F = 0.7. Research limitations/implications: The proposed methods have no limitations and can be applied to solve constrained optimization problems in general, and structural ones in particular. Practical/implications: The proposed techniques can be used to solve real-world problems in engineering. Also, the performance of the proposals is examined using structural engineering problems. Originality/value: The main contributions of this work are to introduce and to evaluate additional local surrogate models; to evaluate the effect of the value of DE's parameter F (which scales the differences between components of candidate solutions) upon each surrogate model; and to perform a more complete set of experiments covering continuous as well as discrete design variables.