Archive-based Multi-Objective Arithmetic Optimization (MAOA)

An Archive-based Multi-Objective Arithmetic Optimization Algorithm for Solving Industrial Engineering Problems
378 Downloads
Updated 11 Oct 2022

View License

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.

Cite As

Nima Khodadadi (2024). Archive-based Multi-Objective Arithmetic Optimization (MAOA) (https://www.mathworks.com/matlabcentral/fileexchange/118923-archive-based-multi-objective-arithmetic-optimization-maoa), MATLAB Central File Exchange. Retrieved .

Khodadadi, Nima, et al. “An Archive-Based Multi-Objective Arithmetic Optimization Algorithm for Solving Industrial Engineering Problems.” IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), 2022, pp. 1–1, doi:10.1109/access.2022.3212081.

View more styles
MATLAB Release Compatibility
Created with R2022b
Compatible with any release
Platform Compatibility
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Version Published Release Notes
1.0.0