Constrained Multi-Modal Multi-Objective Evolutionary Algorithm with Problem Transformation into Two-Objective Subproblems

Real-world optimization problems often have multi-ple conflicting objective functions to be optimized simultaneously. In some of them, there are different Pareto optimal solutions with the same objective function values. Those problems are called multi-modal multi-objective optimization problems (MM...

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Vydáno v:2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS) s. 1 - 6
Hlavní autoři: Tokusaka, Teruhiko, Masuyama, Naoki, Nojima, Yusuke
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 09.11.2024
Témata:
Constrained dominance principle
Constrained multi-modal multi-objective optimization problems
Constraint handling
Evolutionary computation
Linear programming
Multi-objective evolutionary algorithms
Optimization
Pareto optimization
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Abstract Real-world optimization problems often have multi-ple conflicting objective functions to be optimized simultaneously. In some of them, there are different Pareto optimal solutions with the same objective function values. Those problems are called multi-modal multi-objective optimization problems (MMOPs). For MMOPs, we proposed a decomposition-based multi-modal multi-objective evolutionary algorithm called MM2T in our previous study. However, MM2T does not consider constraints and thus cannot solve constrained MMOPs (CMMOPs). To apply MM2T to CMMOPs, we introduce the constrained dominance principle (CDP) into MM2T. We examine the search performance of MM2T with CDP through computational experiments.
AbstractList Real-world optimization problems often have multi-ple conflicting objective functions to be optimized simultaneously. In some of them, there are different Pareto optimal solutions with the same objective function values. Those problems are called multi-modal multi-objective optimization problems (MMOPs). For MMOPs, we proposed a decomposition-based multi-modal multi-objective evolutionary algorithm called MM2T in our previous study. However, MM2T does not consider constraints and thus cannot solve constrained MMOPs (CMMOPs). To apply MM2T to CMMOPs, we introduce the constrained dominance principle (CDP) into MM2T. We examine the search performance of MM2T with CDP through computational experiments.
Author Masuyama, Naoki
Tokusaka, Teruhiko
Nojima, Yusuke
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  givenname: Teruhiko
  surname: Tokusaka
  fullname: Tokusaka, Teruhiko
  email: sc24892v@st.omu.ac.jp
  organization: Graduate School of Informatics, Osaka Metropolitan University,Osaka,Japan
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  givenname: Naoki
  surname: Masuyama
  fullname: Masuyama, Naoki
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  organization: Graduate School of Informatics, Osaka Metropolitan University,Osaka,Japan
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  givenname: Yusuke
  surname: Nojima
  fullname: Nojima, Yusuke
  email: nojima@omu.ac.jp
  organization: Graduate School of Informatics, Osaka Metropolitan University,Osaka,Japan
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Snippet Real-world optimization problems often have multi-ple conflicting objective functions to be optimized simultaneously. In some of them, there are different...
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SubjectTerms Constrained dominance principle
Constrained multi-modal multi-objective optimization problems
Constraint handling
Evolutionary computation
Linear programming
Multi-objective evolutionary algorithms
Optimization
Pareto optimization
Title Constrained Multi-Modal Multi-Objective Evolutionary Algorithm with Problem Transformation into Two-Objective Subproblems
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