Identifying Pareto Fronts Reliably Using a Multistage Reference-Vector-Based Framework

Evolutionary multiobjective and many-objective optimization (EMO and EMaO) algorithms are increasingly used to identify the true shape and location of the Pareto-optimal front using a few representative well-converged and well-distributed solutions. The reason for their popularity is due to their ab...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 28; no. 1; pp. 252 - 266
Main Authors: Deb, Kalyanmoy, Lopes, Claudio Lucio do Val, Martins, Flávio Vinícius Cruzeiro, Wanner, Elizabeth Fialho
Format: Journal Article
Language:English
Published: New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.02.2024
Subjects:
Algorithms
Convergence
Decision analysis
Evolutionary algorithms
Multiple objective analysis
Pareto optimum
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:Evolutionary multiobjective and many-objective optimization (EMO and EMaO) algorithms are increasingly used to identify the true shape and location of the Pareto-optimal front using a few representative well-converged and well-distributed solutions. The reason for their popularity is due to their ability to provide a better understanding of objective relationships for optimal solutions, and also to facilitate the choice of a preferred solution using an interactive or post-optimal multicriterion decision analysis. However, since EMO and EMaO algorithms are stochastic, a single application may not provide a true representative set with a desired number of Pareto solutions reliably in repetitive runs and importantly with a well-distributed set of solutions. In this article, we propose a multistage framework involving reference-vector-based evolutionary multi- and many-objective algorithms (MuSt-EMO and MuSt-EMaO) that attempts to recursively rectify shortcomings of previous stages by careful executions of subsequent stages so that a prescribed number of well-distributed and well-converged solutions are achieved at the end. The proposed multistage approach is implemented to a number of popular reference vector-based EMO/EMaO algorithms and is applied on various multi- and many-objective test and real-world problems.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2023.3246922