Gradient-Informed Pareto-Based Multi-Objective Binary Topology Optimization

Multi-objective topology optimization (TO) problems frequently arise in practical engineering applications, necessitating the identification of Pareto-optimal solutions. This article introduces a gradientin-formed Pareto-based multi-objective TO algorithm with binary decision variables, called GPBTO...

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Bibliographic Details
Published in:IEEE access Vol. 13; p. 1
Main Authors: Lucchini, Francesco, Torchio, Riccardo, Alotto, Piergiorgio
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
binary linear programming
Design engineering
Finite element analysis
gradient-search
Integer linear programming
Integer programming
Interpolation
large-scale multi-objective optimization
Linear programming
Material properties
Minimization
Multiple objective analysis
Optimization
Pareto optimum
Pareto-optimal solutions
Topology
Topology optimization
Topology optimization (TO)
Vectors
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Multi-objective topology optimization (TO) problems frequently arise in practical engineering applications, necessitating the identification of Pareto-optimal solutions. This article introduces a gradientin-formed Pareto-based multi-objective TO algorithm with binary decision variables, called GPBTO, tailored to constrained bi-objective optimization problems (CBOPs), a common scenario in engineering design. By leveraging a binary decision space and incorporating a linearization step for objectives and constraints, the method enables the use of efficient integer linear programming (ILP) techniques for evolving the decision vector. Unlike traditional weighted sum (WS) approaches, which are widely used in TO despite their known limitations, GPBTO provides an alternative that integrates gradient-based formulations while facilitating the identification of Pareto-optimal solutions. While WS remains a dominant method in TO, GPBTO represents a promising alternative for cases where Pareto-based solutions are desirable. To the best of the authors' knowledge, this is the first attempt to integrate Pareto-based TO with binary variables and gradient information, highlighting its potential for further exploration and application.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3570329