Vol.12, No.3, August 2023.                                                                                                                                                                               ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

An Overview and Comparison of Selected State-of-the-Art Algorithms Inspired by Nature


Marko Gulić, Martina Žuškin, Vilim Kvaternik


© 2023 Marko Gulić, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 12, Issue 3, Pages 1281-1293, ISSN 2217-8309, DOI: 10.18421/TEM123-07, August 2023.


Received: 07 May 2023.

Revised:   26 June 2023.
Accepted: 01 August 2023.
Published: 28 August 2023.




Optimization is essential in various fields such as finance, transportation, energy, and health care. However, solving real optimization problems, especially nondeterministic polynomial, requires considerable computational resources. Metaheuristics provide fast and cost-effective solutions to these problems. In this paper, eight state-of-the-art nature-inspired metaheuristic algorithms that have demonstrated excellent performance are compared in detail. In addition, a novel tournament procedure has been proposed to produce a quality ranking of selected metaheuristic algorithms, which are compared based on their optimization results, even if they were not originally tested with the same set of test functions, but only partially. The selected algorithms are evaluated using thirty-two test functions, which is a representative sample size. The evaluation also showed that while one algorithm produced the best overall results, this does not mean that this algorithm is the best for solving each function. This also highlights the need for further research in metaheuristic algorithms.


Keywords –Optimization, nature-inspired metaheuristics, comparison of metaheuristics, optimization functions.



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