A Consensus-Based Algorithm for Multi-Objective Optimization and Its Mean-Field Description

Giacomo Borghi; Michael Herty; Lorenzo Pareschi

doi:10.1109/CDC51059.2022.9993095

A Consensus-Based Algorithm for Multi-Objective Optimization and Its Mean-Field Description

Giacomo Borghi^*, Michael Herty, Lorenzo Pareschi

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

We present a multi-agent algorithm for multi-objective optimization problems, which extends the class of consensus-based optimization methods and relies on a scalarization strategy. The optimization is achieved by a set of interacting agents exploring the search space and attempting to solve all scalar sub-problems simultaneously. We show that those dynamics are described by a mean-field model, which is suitable for a theoretical analysis of the algorithm convergence. Numerical results show the validity of the proposed method.

Original language	English
Title of host publication	61st IEEE Conference on Decision and Control 2022
Publisher	IEEE
Pages	4131-4136
Number of pages	6
ISBN (Electronic)	9781665467612
DOIs	https://doi.org/10.1109/CDC51059.2022.9993095
Publication status	Published - 10 Jan 2023
Event	61st IEEE Conference on Decision and Control 2022 - Cancun, Mexico Duration: 6 Dec 2022 → 9 Dec 2022

Conference

Conference	61st IEEE Conference on Decision and Control 2022
Abbreviated title	CDC 2022
Country/Territory	Mexico
City	Cancun
Period	6/12/22 → 9/12/22

ASJC Scopus subject areas

Control and Systems Engineering
Modelling and Simulation
Control and Optimization

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10.1109/CDC51059.2022.9993095

Cite this

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title = "A Consensus-Based Algorithm for Multi-Objective Optimization and Its Mean-Field Description",

abstract = "We present a multi-agent algorithm for multi-objective optimization problems, which extends the class of consensus-based optimization methods and relies on a scalarization strategy. The optimization is achieved by a set of interacting agents exploring the search space and attempting to solve all scalar sub-problems simultaneously. We show that those dynamics are described by a mean-field model, which is suitable for a theoretical analysis of the algorithm convergence. Numerical results show the validity of the proposed method.",

author = "Giacomo Borghi and Michael Herty and Lorenzo Pareschi",

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AU - Borghi, Giacomo

AU - Herty, Michael

AU - Pareschi, Lorenzo

PY - 2023/1/10

Y1 - 2023/1/10

N2 - We present a multi-agent algorithm for multi-objective optimization problems, which extends the class of consensus-based optimization methods and relies on a scalarization strategy. The optimization is achieved by a set of interacting agents exploring the search space and attempting to solve all scalar sub-problems simultaneously. We show that those dynamics are described by a mean-field model, which is suitable for a theoretical analysis of the algorithm convergence. Numerical results show the validity of the proposed method.

AB - We present a multi-agent algorithm for multi-objective optimization problems, which extends the class of consensus-based optimization methods and relies on a scalarization strategy. The optimization is achieved by a set of interacting agents exploring the search space and attempting to solve all scalar sub-problems simultaneously. We show that those dynamics are described by a mean-field model, which is suitable for a theoretical analysis of the algorithm convergence. Numerical results show the validity of the proposed method.

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U2 - 10.1109/CDC51059.2022.9993095

DO - 10.1109/CDC51059.2022.9993095

M3 - Conference contribution

AN - SCOPUS:85136166850

SP - 4131

EP - 4136

BT - 61st IEEE Conference on Decision and Control 2022

PB - IEEE

T2 - 61st IEEE Conference on Decision and Control 2022

Y2 - 6 December 2022 through 9 December 2022

ER -

A Consensus-Based Algorithm for Multi-Objective Optimization and Its Mean-Field Description

Abstract

Conference

ASJC Scopus subject areas

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