An Archive-based Multi-Objective Arithmetic Optimization Algorithm for Solving Industrial Engineering Problems

This research proposes an Archive-based Multi-Objective Arithmetic Optimization Algorithm (MAOA) as an alternative to the recently established Arithmetic Optimization Algorithm (AOA) for multi-objective problems (MAOA). The original AOA approach was based on the distribution behavior of vital mathem...

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Vydáno v:IEEE access Ročník 10; s. 1
Hlavní autoři: Khodadadi, Nima, Abualigh, Laith, EI-Kenawy, EI-Sayed M., Snasel, Vaclav, Mirjalili, Seyedali
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Archive-based Multi-Objective Arithmetic Optimization Algorithm (MAOA)
Archives & records
Arithmetic
Arithmetic Optimization Algorithm (AOA)
Design engineering
Engineering Optimization
Functions (mathematics)
Genetic algorithms
Heuristic algorithms
Industrial engineering
Linear programming
Mathematical analysis
Metaheuristics
Multi-objective Problems
Multiple objective analysis
Multiplication
Operators (mathematics)
Optimization
Optimization algorithms
Pareto optimization
Pareto optimum
Particle swarm optimization
Search problems
Subtraction
ISSN:2169-3536, 2169-3536
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Popis
Shrnutí:This research proposes an Archive-based Multi-Objective Arithmetic Optimization Algorithm (MAOA) as an alternative to the recently established Arithmetic Optimization Algorithm (AOA) for multi-objective problems (MAOA). The original AOA approach was based on the distribution behavior of vital mathematical arithmetic operators, such as multiplication, division, subtraction, and addition. The idea of the archive is introduced in MAOA, and it may be used to find non-dominated Pareto optimum solutions. The proposed method is tested on seven benchmark functions, ten CEC-2020 mathematic functions, and eight restricted engineering design challenges to determine its suitability for solving real-world engineering difficulties. The experimental findings are compared to five multi-objective optimization methods (Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Slap Swarm Algorithm (MSSA), Multi-Objective Ant Lion Optimizer (MOALO), Multi-Objective Genetic Algorithm (NSGA2) and Multi-Objective Grey Wolf Optimizer (MOGWO) reported in the literature using multiple performance measures. The empirical results show that the proposed MAOA outperforms existing state-of-the-art multi-objective approaches and has a high convergence rate.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3212081