Abstract
The purpose of this paper is to improve efficiency of multi-objective topology optimization using bar-system representation Genetic Algorithm (GA). We propose a new GA using elite initial individuals produced using a SIMP (Solid Isotropic Material with Penalization) method with a weighted sum method. The SIMP method for a multi-objective topology optimization is one of the most established methods that use the sensitivity analysis. Although the SIMP method is easily implemented and it is computationally effective, it may be difficult to find a proper Pareto-optimal set in a multi-objective optimization. In the present paper, GA is adopted to obtain the Pareto-optimal set. To build more evenly distributed global Pareto-optimal set and reduce GA computational effort, new individuals that resemble topology of the Pareto-optimal set of SIMP are introduced for initial pool of GA. The proposed method is applied to a structural topology optimization example and compared with the results of the traditional method that uses standard random initialization for initial pool of GA.
