Metaheuristics for variable-size mixed optimization problems: A unified taxonomy and survey

Many real world optimization problems are formulated as mixed-variable optimization problems (MVOPs) which involve both continuous and discrete variables. MVOPs including dimensional variables are characterized by a variable-size search space. Depending on the values of dimensional variables, the nu...

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Vydáno v:Swarm and evolutionary computation Ročník 89; s. 101642
Hlavní autor: Talbi, El-Ghazali
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.08.2024
Elsevier
Témata:
Computer Science
Decomposition-based optimization
Metaheuristics
Mixed optimization
Mixed variable programming
Mixed-variable optimization problem
Variable-size mixed-variable optimization problem
Variable-space design
Mixed-variable optimization problem
Metaheuristics
Variable-size mixed-variable optimization problem
Mixed optimization
Mixed variable programming
Decomposition-based optimization
Variable-space design
Metaheuristics, Mixed-variable optimization problem, Variable-size mixed-variable optimization problem, Mixed optimization, Mixed variable programming, Variable-space design, Decomposition-based optimization
ISSN:2210-6502
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Abstract Many real world optimization problems are formulated as mixed-variable optimization problems (MVOPs) which involve both continuous and discrete variables. MVOPs including dimensional variables are characterized by a variable-size search space. Depending on the values of dimensional variables, the number and type of the variables of the problem can vary dynamically. MVOPs and variable-size MVOPs (VMVOPs) are difficult to solve and raise a number of scientific challenges in the design of metaheuristics. Standard metaheuristics have been first designed to address continuous or discrete optimization problems, and are not able to tackle VMVOPs in an efficient way. The development of metaheuristics for solving such problems has attracted the attention of many researchers and is increasingly popular. However, to our knowledge there is no well established taxonomy or comprehensive survey for handling this important family of optimization problems. This paper presents an unified taxonomy for metaheuristic solutions for solving VMVOPs in an attempt to provide a common terminology and classification mechanisms. It provides a general mathematical formulation and concepts of VMVOPs, and identifies the various solving methodologies than can be applied in metaheuristics. The advantages, the weaknesses and the limitations of the presented methodologies are discussed. The proposed taxonomy also allows to identify some open research issues which needs further in-depth investigations.
AbstractList Many real world optimization problems are formulated as mixed-variable optimization problems (MVOPs) which involve both continuous and discrete variables. MVOPs including dimensional variables are characterized by a variable-size search space. Depending on the values of dimensional variables, the number and type of the variables of the problem can vary dynamically. MVOPs and variable-size MVOPs (VMVOPs) are difficult to solve and raise a number of scientific challenges in the design of metaheuristics. Standard metaheuristics have been first designed to address continuous or discrete optimization problems, and are not able to tackle VMVOPs in an efficient way. The development of metaheuristics for solving such problems has attracted the attention of many researchers and is increasingly popular. However, to our knowledge there is no well established taxonomy or comprehensive survey for handling this important family of optimization problems. This paper presents an unified taxonomy for metaheuristic solutions for solving VMVOPs in an attempt to provide a common terminology and classification mechanisms. It provides a general mathematical formulation and concepts of VMVOPs, and identifies the various solving methodologies than can be applied in metaheuristics. The advantages, the weaknesses and the limitations of the presented methodologies are discussed. The proposed taxonomy also allows to identify some open research issues which needs further in-depth investigations.
ArticleNumber 101642
Author Talbi, El-Ghazali
Author_xml – sequence: 1
  givenname: El-Ghazali
  orcidid: 0000-0003-4549-1010
  surname: Talbi
  fullname: Talbi, El-Ghazali
  email: el-ghazali.talbi@univ-lille.fr
  organization: Univ. Lille, CNRS, Inria, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France
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Keywords Mixed-variable optimization problem
Metaheuristics
Variable-size mixed-variable optimization problem
Mixed optimization
Mixed variable programming
Decomposition-based optimization
Variable-space design
Metaheuristics, Mixed-variable optimization problem, Variable-size mixed-variable optimization problem, Mixed optimization, Mixed variable programming, Variable-space design, Decomposition-based optimization
Language English
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PublicationTitle Swarm and evolutionary computation
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Snippet Many real world optimization problems are formulated as mixed-variable optimization problems (MVOPs) which involve both continuous and discrete variables....
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StartPage 101642
SubjectTerms Computer Science
Decomposition-based optimization
Metaheuristics
Mixed optimization
Mixed variable programming
Mixed-variable optimization problem
Variable-size mixed-variable optimization problem
Variable-space design
Title Metaheuristics for variable-size mixed optimization problems: A unified taxonomy and survey
URI https://dx.doi.org/10.1016/j.swevo.2024.101642
https://hal.science/hal-04901075
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