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An Improved Archimedes Optimization Algorithm for Solving Optimization Problems

The Archimedes Optimization Algorithm (AOA) algorithm, a multi-agent-based metaheuristic, has garnered attention for its remarkable accuracy in real-world optimization. This research addresses solutions for the inherent limitation of original AOA, notably its susceptibility to uneven exploration and...

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Main Authors: Mohd Ashraf, Ahmad, Islam, Muhammad Shafiqul, Muhammad Ikram, Mohd Rashid
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2024
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/40726/1/An_Improved_Archimedes_Optimization_Algorithm_for_Solving_Optimization_Problems.pdf
http://umpir.ump.edu.my/id/eprint/40726/7/An%20Improved%20Archimedes%20Optimization%20Algorithm.pdf
http://umpir.ump.edu.my/id/eprint/40726/
https://ieeexplore.ieee.org/document/10468411
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spelling my.ump.umpir.407262024-03-22T01:47:51Z http://umpir.ump.edu.my/id/eprint/40726/ An Improved Archimedes Optimization Algorithm for Solving Optimization Problems Mohd Ashraf, Ahmad Islam, Muhammad Shafiqul Muhammad Ikram, Mohd Rashid TK Electrical engineering. Electronics Nuclear engineering The Archimedes Optimization Algorithm (AOA) algorithm, a multi-agent-based metaheuristic, has garnered attention for its remarkable accuracy in real-world optimization. This research addresses solutions for the inherent limitation of original AOA, notably its susceptibility to uneven exploration and exploitation phases and its propensity to become ensnared in local optima. To overcome these limitations, we employ two strategies: the modification of the density decreasing factor and the introduction of a safe updating mechanism inspired by game theory. These enhancements are subjected to rigorous evaluation using 23 benchmark functions, and their performance is compared against that of the original AOA and other prominent algorithms, including the Multiverse Optimization (MVO), Grasshopper Optimization Algorithm (GOA), Sine Cosine Algorithm (SCA), and Ant Lion Optimizer (ALO). The test results reveal significant improvements achieved by the newly proposed improved AOA (IAOA), surpassing the performance of the original AOA in 69% of the optimization cases among the 23 test functions. It is noteworthy that it also outperformed the other mentioned algorithms. The potential of the proposed algorithm as an effective tool for addressing real-world optimization challenges is underscored by these encouraging findings, adhering to research conventions. IEEE 2024 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40726/1/An_Improved_Archimedes_Optimization_Algorithm_for_Solving_Optimization_Problems.pdf pdf en http://umpir.ump.edu.my/id/eprint/40726/7/An%20Improved%20Archimedes%20Optimization%20Algorithm.pdf Mohd Ashraf, Ahmad and Islam, Muhammad Shafiqul and Muhammad Ikram, Mohd Rashid (2024) An Improved Archimedes Optimization Algorithm for Solving Optimization Problems. In: 2023 IEEE 8th International Conference on Recent Advances and Innovations in Engineering (ICRAIE). IEEE 8th International Conference on Recent Advances and Innovations in Engineering (ICRAIE) , 02-03 December 2023 , Kuala Lumpur, Malaysia. pp. 1-5. (59459). ISBN 979-8-3503-1552-3 https://ieeexplore.ieee.org/document/10468411
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Mohd Ashraf, Ahmad
Islam, Muhammad Shafiqul
Muhammad Ikram, Mohd Rashid
An Improved Archimedes Optimization Algorithm for Solving Optimization Problems
description The Archimedes Optimization Algorithm (AOA) algorithm, a multi-agent-based metaheuristic, has garnered attention for its remarkable accuracy in real-world optimization. This research addresses solutions for the inherent limitation of original AOA, notably its susceptibility to uneven exploration and exploitation phases and its propensity to become ensnared in local optima. To overcome these limitations, we employ two strategies: the modification of the density decreasing factor and the introduction of a safe updating mechanism inspired by game theory. These enhancements are subjected to rigorous evaluation using 23 benchmark functions, and their performance is compared against that of the original AOA and other prominent algorithms, including the Multiverse Optimization (MVO), Grasshopper Optimization Algorithm (GOA), Sine Cosine Algorithm (SCA), and Ant Lion Optimizer (ALO). The test results reveal significant improvements achieved by the newly proposed improved AOA (IAOA), surpassing the performance of the original AOA in 69% of the optimization cases among the 23 test functions. It is noteworthy that it also outperformed the other mentioned algorithms. The potential of the proposed algorithm as an effective tool for addressing real-world optimization challenges is underscored by these encouraging findings, adhering to research conventions.
format Conference or Workshop Item
author Mohd Ashraf, Ahmad
Islam, Muhammad Shafiqul
Muhammad Ikram, Mohd Rashid
author_facet Mohd Ashraf, Ahmad
Islam, Muhammad Shafiqul
Muhammad Ikram, Mohd Rashid
author_sort Mohd Ashraf, Ahmad
title An Improved Archimedes Optimization Algorithm for Solving Optimization Problems
title_short An Improved Archimedes Optimization Algorithm for Solving Optimization Problems
title_full An Improved Archimedes Optimization Algorithm for Solving Optimization Problems
title_fullStr An Improved Archimedes Optimization Algorithm for Solving Optimization Problems
title_full_unstemmed An Improved Archimedes Optimization Algorithm for Solving Optimization Problems
title_sort improved archimedes optimization algorithm for solving optimization problems
publisher IEEE
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/40726/1/An_Improved_Archimedes_Optimization_Algorithm_for_Solving_Optimization_Problems.pdf
http://umpir.ump.edu.my/id/eprint/40726/7/An%20Improved%20Archimedes%20Optimization%20Algorithm.pdf
http://umpir.ump.edu.my/id/eprint/40726/
https://ieeexplore.ieee.org/document/10468411
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score 13.232414

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