Pareto Optimization of Energy Absorption of Square Aluminium Columns Using Multi-Objective Genetic Algorithms

Abstract

Multi-objective genetic algorithms (GAs) are used for Pareto-approach optimization of the energy absorption of square aluminium columns both with and without aluminium foam filler. In this way, a new diversity-preserving algorithm is proposed to enhance the performance of multi-objective evolutionary algorithms (MOEAs). The important conflicting energy-absorption objectives that have been considered in this work are weight and absorbed energy. The multi-objective optimization is considered for two different cases of energy absorption, namely, non-filled and foam filled. It is also demonstrated that the results of two-objective optimization include those of single-objective optimization and, therefore, provide more choices for optimal design of energy absorption for square aluminium columns with and without aluminium foam filler. Using the obtained Pareto front, it is further found that non-filled columns are preferred over foam-filled ones when the desired energy absorption is no more than 4.8 kJ for square aluminium columns.