Characterization of Constrained Continuous Multiobjective Optimization Problems: A Performance Space Perspective
Constrained multiobjective optimization has gained much interest in the past few years. However, constrained multiobjective optimization problems (CMOPs) are still unsatisfactorily understood. Consequently, the choice of adequate CMOPs for benchmarking is difficult and lacks a formal background. Thi...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
04.02.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Constrained multiobjective optimization has gained much interest in the past
few years. However, constrained multiobjective optimization problems (CMOPs)
are still unsatisfactorily understood. Consequently, the choice of adequate
CMOPs for benchmarking is difficult and lacks a formal background. This paper
addresses this issue by exploring CMOPs from a performance space perspective.
First, it presents a novel performance assessment approach designed explicitly
for constrained multiobjective optimization. This methodology offers a first
attempt to simultaneously measure the performance in approximating the Pareto
front and constraint satisfaction. Secondly, it proposes an approach to measure
the capability of the given optimization problem to differentiate among
algorithm performances. Finally, this approach is used to contrast eight
frequently used artificial test suites of CMOPs. The experimental results
reveal which suites are more efficient in discerning between three well-known
multiobjective optimization algorithms. Benchmark designers can use these
results to select the most appropriate CMOPs for their needs. |
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DOI: | 10.48550/arxiv.2302.02170 |