Evolutionary Black-box Topology Optimization: Challenges and Promises
Name
evolutionary-black-box-topology-optimization_-challenges-and-promises.pdf
Description
Accepted version
Size
2.32 MB
Format
Adobe PDF
Checksum (MD5)
d82d5955f9b0aa332cc98c6cdf538352
Author(s)
Guirguis, David
Aulig, Nikola
Picelli, Renato
Zhu, Bo
Zhou, Yuqing
Vicente, William
Iorio, Francesco
Olhofer, Markus
Matusiks, Wojciech
Coello Coello, Carlos Artemio
Date Issued
2020
Journal
IEEE Transactions on Evolutionary Computation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
© 1997-2012 IEEE. Black-box topology optimization (BBTO) uses evolutionary algorithms and other soft computing techniques to generate near-optimal topologies of mechanical structures. Although evolutionary algorithms are widely used to compensate the limited applicability of conventional gradient optimization techniques, methods based on BBTO have been criticized due to numerous drawbacks. In this article, we discuss topology optimization as a black-box optimization problem. We review the main BBTO methods, discuss their challenges and present approaches to relax them. Dealing with those challenges effectively can lead to wider applicability of topology optimization, as well as the ability to tackle industrial, highly constrained, nonlinear, many-objective, and multimodal problems. Consequently, future research in this area may open the door for innovating new applications in science and engineering that may go beyond solving classical optimization problems of mechanical structures. Furthermore, algorithms designed for BBTO can be added to existing software toolboxes and packages of topology optimization.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TEVC.2019.2954411
