2017
DOI: 10.1007/978-3-319-54157-0_34
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An Overview of Weighted and Unconstrained Scalarizing Functions

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Cited by 35 publications
(14 citation statements)
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“…Besides exploring new research paths, it is important to gain a deeper understanding of the major algorithms in current use. For example, knowing that some scalarizing functions offer advantages over others [124] is very useful to design good decomposition-based and even indicator-based multi-objective solvers (algorithms based on R2 normally rely on decomposition).…”
Section: Multi-and Many-objective Optimizationmentioning
confidence: 99%
“…Besides exploring new research paths, it is important to gain a deeper understanding of the major algorithms in current use. For example, knowing that some scalarizing functions offer advantages over others [124] is very useful to design good decomposition-based and even indicator-based multi-objective solvers (algorithms based on R2 normally rely on decomposition).…”
Section: Multi-and Many-objective Optimizationmentioning
confidence: 99%
“…In such approaches, the ASF is computed by summing up each objective function f k multiplied by a pre-defined weight w k accounting for the user preferences. Multiple formulations of weighted sums and products exist, 40 and methods have been developed to learn these weights adaptively. 41 Weighted approaches are usually simple to implement, but the challenge lies in finding suitable weight vectors to yield Pareto optimal solutions.…”
Section: Background and Related Workmentioning
confidence: 99%
“…Another challenge in this area is to gain a deeper understanding of the limitations of current MOEAs. For example, knowing that some scalarizing functions offer advantages over others is very useful to design good decomposition-based and even indicator-based MOEAs (see for example [127]). Another interesting idea is to combine components of MOEAs under a single framework that allows to exploit their advantages.…”
Section: Challengesmentioning
confidence: 99%