Quokka swarm optimization: A new nature-inspired metaheuristic optimization algorithm

Problem: Metaheuristics are efficient algorithms designed to address a broad spectrum of optimization challenges and offer satisfactory solutions, even in scenarios of limited processing capability or incomplete information. It has been observed that no single metaheuristic algorithm is universally ideal for all applications. This realization underscores the opportunity for the introduction of new metaheuristic algorithms or enhancements to existing ones. Aim: The aim of this work is to propose Quokka swarm optimization (QSO), a novel nature-inspired metaheuristic optimization technique. The QSO simulates the cooperative behavior of quokka animals, which can be used to address optimization issues. Method: A group of common unconstrained and constrained test functions is employed to demonstrate the strength of the proposed approach. To test the performance of QSO, 43 popular test functions that are used in the optimization were employed as benchmarks. The solutions have been refining their positions in tandem with the ongoing discovery of the best solution. In addition, QSO can substitute the worst quokka with the best child found so far to improve the solutions. Performance comparisons using the Blue monkey swarm optimization, Gray wolf optimization, Biogeography-based optimizer, Artificial bee colony, Particle swarm optimization, and Gravitational search algorithm were also performed. Results: The obtained results showed that QSO is competitive in comparison to the chosen metaheuristic algorithms.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Quokka swarm optimization: A new nature-inspired metaheuristic optimization algorithm ; volume:33 ; number:1 ; year:2024 ; extent:17 ; Journal of intelligent systems ; 33, Heft 1 (2024) (gesamt 17)

Contributors, Places and Time

• Creator: AL-kubaisy, Wijdan Jaber ; AL-Khateeb, Belal

Further information

• DOI: 10.1515/jisys-2024-0051
• URN: urn:nbn:de:101:1-2406221537284.570340078880
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:56 AM CEST