Material stiffness optimization for homogenizing contact stress distribution based on particle swarm optimization using elite opposition-based learning mutation

Abstract

Rapid advances in computer science and material science have made intelligent algorithms promising for solving material design problems. Particle swarm optimization (PSO) algorithm, as a typical swarm intelligence algorithm, is utilized in this paper as the tool to solve the problem of material stiffness optimization for homogenizing the contact stress distribution. In order to achieve an effective and efficient solution, the opposition-based learning (OBL) technique is introduced and combined with PSO to increases its exploration ability. Two mutation strategies, namely, dimension-by-dimension opposition-based learning and multi-dimensional random opposition-based learning mutation strategies, are further proposed to increase the population diversity of PSO. On this basis, algorithms named DEOBL-PSO and MREOBL-PSO are developed. The developed DEOBL-PSO and MREOBL-PSO are then applied to the field of nonlinear contact in engineering, and the specific problem of material stiffness optimization for homogenizing the contact stress distribution is solved. Impressive results are obtained. The contact stress distributing uniformity is substantially improved by material stiffness optimization using DEOBL-PSO and MREOBL-PSO. Additionally, new relation between material stiffness distribution and contact stress distribution is observed, and the effect of the material stiffness variation range on the contact stress distribution is further explored.