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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10251964" target="_blank" >RIV/61989100:27240/22:10251964 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9911646" target="_blank" >https://ieeexplore.ieee.org/document/9911646</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2022.3212081" target="_blank" >10.1109/ACCESS.2022.3212081</a>

Result language
angličtina
Original language name
An Archive-Based Multi-Objective Arithmetic Optimization Algorithm for Solving Industrial Engineering Problems
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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