Non-dominated Sorting Advanced Butterfly Optimization Algorithm for Multi-objective Problems

This paper uses the Butterfly Optimization Algorithm (BOA) with dominated sorting and crowding distance mechanisms to solve multi-objective optimization problems. There is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective version...

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Vydáno v:Journal of bionics engineering Ročník 20; číslo 2; s. 819 - 843
Hlavní autoři: Sharma, Sushmita, Khodadadi, Nima, Saha, Apu Kumar, Gharehchopogh, Farhad Soleimanian, Mirjalili, Seyedali
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Nature Singapore 01.03.2023
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Biochemical Engineering
Bioinformatics
Biomaterials
Biomedical Engineering and Bioengineering
Biomedical Engineering/Biotechnology
Crowding
Engineering
Immunoglobulin D
Multiple objective analysis
Pareto optimization
Performance measurement
Problem solving
Research Article
Multi-objective problems
Crowding distance
Butterfly optimization algorithm
Non-dominated sorting
ISSN:1672-6529, 2543-2141
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Abstract This paper uses the Butterfly Optimization Algorithm (BOA) with dominated sorting and crowding distance mechanisms to solve multi-objective optimization problems. There is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective version. Due to better coverage and a well-distributed Pareto front, non-dominant rankings are applied to the modified BOA using the crowding distance strategy. Seven benchmark functions and eight real-world problems have been used to test the performance of multi-objective non-dominated advanced BOA (MONSBOA), including unconstrained, constrained, and real-world design multiple-objective, highly nonlinear constraint problems. Various performance metrics, such as Generational Distance (GD), Inverted Generational Distance (IGD), Maximum Spread (MS), and Spacing (S), have been used for performance comparison. It is demonstrated that the new MONSBOA algorithm is better than the compared algorithms in more than 80% occasions in solving problems with a variety of linear, nonlinear, continuous, and discrete characteristics based on the Pareto front when compared quantitatively. From all the analysis, it may be concluded that the suggested MONSBOA is capable of producing high-quality Pareto fronts with very competitive results with rapid convergence.
AbstractList This paper uses the Butterfly Optimization Algorithm (BOA) with dominated sorting and crowding distance mechanisms to solve multi-objective optimization problems. There is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective version. Due to better coverage and a well-distributed Pareto front, non-dominant rankings are applied to the modified BOA using the crowding distance strategy. Seven benchmark functions and eight real-world problems have been used to test the performance of multi-objective non-dominated advanced BOA (MONSBOA), including unconstrained, constrained, and real-world design multiple-objective, highly nonlinear constraint problems. Various performance metrics, such as Generational Distance (GD), Inverted Generational Distance (IGD), Maximum Spread (MS), and Spacing (S), have been used for performance comparison. It is demonstrated that the new MONSBOA algorithm is better than the compared algorithms in more than 80% occasions in solving problems with a variety of linear, nonlinear, continuous, and discrete characteristics based on the Pareto front when compared quantitatively. From all the analysis, it may be concluded that the suggested MONSBOA is capable of producing high-quality Pareto fronts with very competitive results with rapid convergence.
Author Gharehchopogh, Farhad Soleimanian
Khodadadi, Nima
Sharma, Sushmita
Saha, Apu Kumar
Mirjalili, Seyedali
Author_xml – sequence: 1
  givenname: Sushmita
  surname: Sharma
  fullname: Sharma, Sushmita
  organization: Department of Mathematics, National Institute of Technology Agartala
– sequence: 2
  givenname: Nima
  surname: Khodadadi
  fullname: Khodadadi, Nima
  organization: Department of Civil and Environment Engineering, Florida International University Miami
– sequence: 3
  givenname: Apu Kumar
  orcidid: 0000-0002-3475-018X
  surname: Saha
  fullname: Saha, Apu Kumar
  email: apusaha_nita@yahoo.co.in
  organization: Department of Mathematics, National Institute of Technology Agartala
– sequence: 4
  givenname: Farhad Soleimanian
  surname: Gharehchopogh
  fullname: Gharehchopogh, Farhad Soleimanian
  organization: Department of Computer Engineering, Urmia Branch, Islamic Azad University
– sequence: 5
  givenname: Seyedali
  surname: Mirjalili
  fullname: Mirjalili, Seyedali
  organization: Centre for Artificial Intelligence Research and Optimization, Torrens University, Yonsei Frontier Lab, Yonsei University
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Jilin University 2022.
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Keywords Multi-objective problems
Crowding distance
Butterfly optimization algorithm
Non-dominated sorting
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SubjectTerms Algorithms
Artificial Intelligence
Biochemical Engineering
Bioinformatics
Biomaterials
Biomedical Engineering and Bioengineering
Biomedical Engineering/Biotechnology
Crowding
Engineering
Immunoglobulin D
Multiple objective analysis
Pareto optimization
Performance measurement
Problem solving
Research Article
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Title Non-dominated Sorting Advanced Butterfly Optimization Algorithm for Multi-objective Problems
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