Multifactorial optimization using Artificial Bee Colony and its application to Car Structure Design Optimization
Gen Yokoya, Heng Xiao, Toshiharu Hatanaka · 2019
Multifactorial optimization (MFO) has attracted attention in the field of evolutionary computation as the third trial where is utilizing multiple search points to optimize multitask optimization. MFO is a method that will optimize multiple individual tasks simultaneously with using some kinds of relation among the target tasks. To make the search efficient, in previous research, we developed a novel MFO algorithm named Task Selective Artificial Bee Colony (TSABC), an improvement of TSABC by introducing a procedure like Firefly Algorithm (FA) is proposed. Then, by applying the proposed method to the real-world car structure design optimization problem, the effectiveness of the proposed method is presented.