MaOAOA: A Novel Many‐Objective Arithmetic Optimization Algorithm for Solving Engineering Problems

ABSTRACT Currently, the use of multi‐objective optimization algorithms has been applied in many fields to find the efficient solution of the multiple objective optimization problems (MOPs). However, this reduces their efficiency when addressing MaOPs, which are problems that contain more than three...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Engineering reports (Hoboken, N.J.) Ročník 7; číslo 3
Hlavní autoři: Jangir, Pradeep, Arpita, Pandya, Sundaram B., G., Gulothungan, Khishe, Mohammad, Trivedi, Bhargavi Indrajit
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 01.03.2025
Wiley
Témata:
Algorithms
Arithmetic
Convergence
Decision making
Decomposition
Genetic algorithms
information feedback mechanism
many‐objective arithmetic optimization algorithm
many‐objective optimization
Mathematical analysis
metaheuristic algorithm
Multiple objective analysis
Objectives
Optimization
Pareto optimality
Pareto optimum
Variables
ISSN:2577-8196, 2577-8196
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:ABSTRACT Currently, the use of multi‐objective optimization algorithms has been applied in many fields to find the efficient solution of the multiple objective optimization problems (MOPs). However, this reduces their efficiency when addressing MaOPs, which are problems that contain more than three objectives; this is because the portion of the Pareto frontier solutions tends to increase exponentially with the number of objectives. This paper aims at overcoming this problem by proposing a new Many‐Objective Arithmetic Optimization Algorithm (MaOAOA) that incorporates a reference point, niche preservation, and an information feedback mechanism (IFM). They did this in a manner that splits the convergence and the diversity phases in the middle of the cycle. The first phase deals with the convergence using a reference point approach, which aims to move the population towards the true Pareto Front. However, the diversity phase of the MaOAOA uses a niche preserve to the archive truncation method in the population, thus guaranteeing that the population is spread out properly along the actual Pareto front. These stages are mutual; that is, the convergence stage supports the diversity stage, and they are balanced by an (IFM) approach. The experimental results show that MaOAOA outperforms several approaches, including MaOTLBO, NSGA‐III, MaOPSO, and MOEA/D‐DRW, in terms of GD, IGD, SP, SD, HV, and RT metrics. This can be seen from the MaF1‐MaF15 test problems, especially with four, seven, and nine objectives, and five real‐world problems that include RWMaOP1 to RWMaOP5. The findings indicate that MaOAOA outperforms the other algorithms in most of the test cases analyzed in this study. MaOAOA introduces an innovative many‐objective optimization framework combining an Information Feedback Mechanism, reference point‐based selection, and niche preservation strategies. Its effectiveness surpasses leading algorithms in convergence, diversity, and computational efficiency across benchmark and real‐world engineering problems, establishing it as a robust solution for high‐dimensional optimization challenges.
Bibliografie:The authors received no specific funding for this work.
Funding
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2577-8196
2577-8196
DOI:10.1002/eng2.70077