MOJMA: A novel multi-objective optimization algorithm based Java Macaque Behavior Model

We introduce the Multi-objective Java Macaque Algorithm for tackling complex multi-objective optimization (MOP) problems. Inspired by the natural behavior of Java Macaque monkeys, the algorithm employs a unique selection strategy based on social hierarchy, with multiple search agents organized into...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	AIMS mathematics Jg. 8; H. 12; S. 30244 - 30268
Hauptverfasser:	Karunanidy, Dinesh, Ramalingam, Rajakumar, Basheer, Shakila, Mahadevan, Nandhini, Rashid, Mamoon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	AIMS Press 01.01.2023
Schlagworte:	continuous optimization problem discrete optimization problem java macaque algorithm multi-objective optimization nature-inspired optimization
ISSN:	2473-6988, 2473-6988
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	We introduce the Multi-objective Java Macaque Algorithm for tackling complex multi-objective optimization (MOP) problems. Inspired by the natural behavior of Java Macaque monkeys, the algorithm employs a unique selection strategy based on social hierarchy, with multiple search agents organized into multi-group populations. It includes male replacement strategies and a learning process to balance intensification and diversification. Multiple decision-making parameters manage trade-offs between potential solutions. Experimental results on real-time MOP problems, including discrete and continuous optimization, demonstrate the algorithm's effectiveness with a 0.9% convergence rate, outperforming the MEDA/D algorithm's 0.98%. This novel approach shows promise for addressing MOP complexities in practical applications.
ISSN:	2473-6988 2473-6988
DOI:	10.3934/math.20231545