Simultaneous optimization of discrete cross-sections and nodal coordinates for spatial trusses combining time-window particle swarm optimization and covariance matrix adaptation evolution strategy

Abstract

A method that combines time-window particle swarm optimization (TWPSO) and covariance matrix adaptation evolution strategy (CMA-ES) is applied to the simultaneous optimization problem of trusses in which the cross-sectional areas and the nodal positions are discrete variables. To accelerate the convergence of TWPSO, the change rate of the member cross-sectional area is modified. In addition, to improve the solution search performance of CMA-ES, the variance of the variable distribution is modified. The solution obtained by TWPSO, which shares global information in the solution space with the entire swarm, is utilized as the initial solution for CMA-ES, which has solution improvement performance against local optimal solutions. Rational optimal solutions can be obtained even for three-dimensional trusses with hundreds of design variables using the proposed method.