Application of artificial bee colony algorithm to topology optimization for dynamic stiffness problems

Abstract

The artificial bee colony algorithm (ABCA) was first adopted in topology optimization for dynamic problems. The objective was to obtain a structure with the highest fundamental natural frequency in a certain amount of material, based on the contributed structural sensitivity of each element calculated by the waggle index and eigenvalue. The waggle index update rule, evaluation method of fitness values, and changing filtering size scheme are suggested for obtaining a stable and robust optimal topology based on the ABCA. Examples are provided to examine the applicability and effectiveness of the ABCA compared to bi-directional evolutionary structural optimization (BESO). The following conclusions are obtained through the results of examples based on the ABCA; (1) the ABCA, using the three suggested methods, is very applicable and effective in topology optimization for obtaining a stable and robust optimal layout. (2) It is found that the natural frequencies of the ABCA are always higher than those of the BESO, and average convergence rates of the ABCA are similar or faster than those of the BESO. (3) The optimal topology from the ABCA is nearly obtained in a half stage of the convergence iteration, since volume constraint is applied from the beginning.