A Comparative Study of Differential Evolution Variants in Constrained Structural Optimization - Georgioudakis, Manolis; Plevris, Vagelis (Frontiers in Built Environment, 2020)
Differential evolution (DE) is a population-based metaheuristic search algorithm that optimizes a problem by iteratively improving a candidate solution based on an evolutionary process. Such algorithms make few or no assumptions about the underlying optimization problem and can quickly explore very large design spaces. DE is arguably one of the most versatile and stable population-based search algorithms that exhibits robustness to multi-modal problems. In the field of structural engineering, most practical optimization problems are associated with one or several behavioral constraints. Constrained optimization problems are quite challenging to solve due to their complexity and high nonlinearity. In this work we examine the performance of several DE variants, namely the standard DE, the composite DE (CODE), the adaptive DE with optional external archive (JADE) and the self-adaptive DE (JDE and SADE), for handling constrained structural optimization problems associated with truss structures. The performance of each DE variant is evaluated by using five well-known benchmark structures in 2D and 3D. The evaluation is done on the basis of final optimum result and the rate of convergence. Valuable conclusions are obtained from the statistical analysis which can help a structural engineer in practice to choose the suitable algorithm for such kind of problems.