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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Vydáno v:	IEEE access Ročník 13; s. 1
Hlavní autoři:	Lucchini, Francesco, Torchio, Riccardo, Alotto, Piergiorgio
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.01.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Abstract	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.
AbstractList	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. Multi-objective topology optimization (TO) problems frequently arise in practical engineering applications, necessitating the identification of Pareto-optimal solutions. This article introduces a gradient-informed 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.
Author	Alotto, Piergiorgio Torchio, Riccardo Lucchini, Francesco
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SubjectTerms	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
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Title	Gradient-Informed Pareto-Based Multi-Objective Binary Topology Optimization
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