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...

Celý popis

Uloženo v:
Podrobná bibliografie
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1672-6529
2543-2141
DOI:10.1007/s42235-022-00288-9