|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Producing Smart Pareto Sets for Multi-Objective Topology Optimisation Problems
If you have a question about this talk, please contact Mari Huhtala.
To date the design of structures via topology optimisation methods has mainly focused on single-objective problems. However, real-world design problems usually involve several different objectives, most of which counteract each other. This work presents an updated smart normal constraint method, which is combined with a bi-directional evolutionary structural optimisation algorithm for multi-objective topology optimisation. The smart normal constraints method has been modified by further restricting the feasible design space for each optimisation run such that dominant and redundant points are not found. The algorithm is tested on several different structural optimisation problems. A number of different structural objectives are analysed, namely compliance, dynamic and buckling objectives. Therefore, the method is shown to be capable of solving various types of multi-objective structural optimisation problems. The goal of this work is to show that smart Pareto sets can be produced for complex topology optimisation problems. Furthermore, this research hopes to highlight the gap in the literature of topology optimisation for multi-objective problems.
This talk is part of the Engineering Design Centre Seminars series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsAll Biological Anthropology Seminars and Events Institute of Continuing Education London Office of Tibet
Other talksScientific habits circa 1900 Excavating the archive: expectant heirs in the Chancery decree rolls, 1596-1640 CCIMI special lecture: Mathematics Enters the Picture Mathematical prediction of a driver's subjective assessment of vehicle dynamics How to quickly generate a nice hyperbolic element Text mining for public health reviews (The Robot Analyst)