Topology Optimisation Using MPBILs and Multi-Grid Ground Element

• Main Authors: Suwin Sleesongsom, Sujin Bureerat
• Format: Article
• Language: English
• Published: MDPI AG 2018-02-01
• Series: Applied Sciences
• Subjects: topology optimization; multi-objective optimization; opposite-based evolutionary algorithm; population-based incremental learning; adaptive learning rate
• Online Access: http://www.mdpi.com/2076-3417/8/2/271

This paper aims to study the comparative performance of original multi-objective population-based incremental learning (MPBIL) and three improvements of MPBIL. The first improvement of original MPBIL is an opposite-based concept, whereas the second and third method enhance the performance of MPBIL using the multi and adaptive learning rate, respectively. Four classic multi-objective structural topology optimization problems are used for testing the performance. Furthermore, these topology optimization problems are improved by the method of multiple resolutions of ground elements, which is called a multi-grid approach (MG). Multi-objective design problems with MG design variables are then posed and tackled by the traditional MPBIL and its improved variants. The results show that using MPBIL with opposite-based concept and MG approach can outperform other MPBIL versions.