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Neural Computing and Applications
Published in: Neural Computing and Applications 1/2022

09-08-2021 | Original Article

A novel meta-heuristic algorithm for solving numerical optimization problems: Ali Baba and the forty thieves

Authors: Malik Braik, Mohammad Hashem Ryalat, Hussein Al-Zoubi

Published in: Neural Computing and Applications | Issue 1/2022

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Abstract

This paper presents a novel meta-heuristic algorithm called Ali Baba and the forty thieves (AFT) for solving global optimization problems. Recall the famous tale of Ali Baba and the forty thieves, where Ali Baba once saw a gang of forty thieves enter a strange cave filled with all kinds of treasures. The strategies pursued by the forty thieves in the search for Ali Baba inspired us to design ideas and underlie the basic concepts to put forward the mathematical models and implement the exploration and exploitation processes of the proposed algorithm. The performance of the AFT algorithm was assessed on a set of basic benchmark test functions and two more challenging benchmarks called IEEE CEC-2017 and IEEE CEC-C06 2019 benchmark test functions. These benchmarks cover simple and complex test functions with various dimensions and levels of complexity. An extensive comparative study was performed between the AFT algorithm and other well-studied algorithms, and the significance of the results was proved by statistical test methods. To study the potential performance of AFT, its further development is discussed and carried out from five aspects. Finally, the applicability of the AFT algorithm was subsequently demonstrated in solving five engineering design problems. The results in both benchmark functions and engineering problems show that the AFT algorithm has stronger performance than other competitors’ algorithms.
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Appendix
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Metadata
Title
A novel meta-heuristic algorithm for solving numerical optimization problems: Ali Baba and the forty thieves
Authors
Malik Braik
Mohammad Hashem Ryalat
Hussein Al-Zoubi
Publication date
09-08-2021
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 1/2022
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-021-06392-x

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