A Multi-Objective Topology Optimization Methodology and its Application to Electromagnetic Actuator Designs
Yilun Li, Lei Liu, Shiyou Yang, Zhuoxiang Ren, Yanhong Ma
Abstract
In this article, a multi-objective topology optimization (MOTO) methodology based on the hybridization of the Non-dominated Sorting Genetic Algorithm II (NSGAII) and Differential Evolutionary (DE) algorithm is proposed. The framework of the proposed hybrid multiobjective optimization (MOO) algorithm is elaborated, and its performances and advantages over existing standard MOO methods are evaluated and demonstrated by solving typical mathematical test functions. To validate the proposed hybrid MOTO methodology, it is applied to the topology optimization of an electromagnetic actuator. Both linear and nonlinear cases are investigated. The numerical results demonstrate that a set of novel topologies with improved multiple objectives is obtained.