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...

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Published in:AIMS mathematics Vol. 8; no. 12; pp. 30244 - 30268
Main Authors: Karunanidy, Dinesh, Ramalingam, Rajakumar, Basheer, Shakila, Mahadevan, Nandhini, Rashid, Mamoon
Format: Journal Article
Language:English
Published: AIMS Press 01.01.2023
Subjects:
continuous optimization problem
discrete optimization problem
java macaque algorithm
multi-objective optimization
nature-inspired optimization
ISSN:2473-6988, 2473-6988
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Abstract 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.
AbstractList 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.
Author Ramalingam, Rajakumar
Karunanidy, Dinesh
Basheer, Shakila
Rashid, Mamoon
Mahadevan, Nandhini
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Snippet We introduce the Multi-objective Java Macaque Algorithm for tackling complex multi-objective optimization (MOP) problems. Inspired by the natural behavior of...
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SubjectTerms continuous optimization problem
discrete optimization problem
java macaque algorithm
multi-objective optimization
nature-inspired optimization
Title MOJMA: A novel multi-objective optimization algorithm based Java Macaque Behavior Model
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