Abstract
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.
Highlights
The first question that always arises at pre-process stage, when using a ground element approach for topology optimization, is:What the best ground element resolution for a design problem should be? As a result, we investigate using several sets of ground elements when performing optimization, which we term the multi-grid design approach (MG)
The use of multi-objective population-based incremental learning (MPBIL) in combination with the multi-grid approach (MG) approach is well capable of solving multi-objective structural topology optimization
The resulting topologies obtained from using OMPBIL are close to those obtained from the classical gradient-based approach
Summary
The first question that always arises at pre-process stage, when using a ground element approach for topology optimization, is:What the best ground element resolution for a design problem should be? As a result, we investigate using several sets of ground elements when performing optimization, which we term the multi-grid design approach (MG). The first question that always arises at pre-process stage, when using a ground element approach for topology optimization, is:What the best ground element resolution for a design problem should be? The second question arises due to an opposition-based concept that could potentially improve the search performance of the evolutionary algorithm (EA) [4,5,6,7]; the multi-objective population-based incremental learning (MPBIL) was the best optimizer [8]. Performance for a single objective, which is called the opposition-based concept PBIL(OPBIL) [9], whereas the multi-objective optimization is called opposite-based, multi-objective, population-based incremental learning (OMPBIL) [3]. OMPBIL with a multi-grid approach has been used to solve partial topology optimization of morphing aircraft wings, and it promotes better results than the original multi-objective population-based incremental learning (MPBIL) with a single grid element.
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