MOJMA: A novel multi-objective optimization algorithm based Java Macaque Behavior Model-Reference-Cited by-同舟云学术

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

Published:2023 Issue:12 Volume:8 Page:30244-30268
ISSN:2473-6988
Container-title:AIMS Mathematics
language:
Short-container-title:MATH

Author:

Karunanidy Dinesh¹,Ramalingam Rajakumar²,Basheer Shakila³,Mahadevan Nandhini⁴,Rashid Mamoon⁵

Affiliation:

1. Department of Computer Science & Technology, Madanapalle Institute of Technology & Science, Madanapalle 517325, India

2. Centre for Automation, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, TamilNadu, India

3. Department of Information Systems, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. BOX 84428, Riyadh 11671, Saudi Arabia

4. Department of Computer Science and Engineering (Data Science), Madanapalle Institute of Technology & Science, Madanapalle 517325, India

5. Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, 411048, India

Abstract

<abstract> <p>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.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

General Mathematics

Reference50 articles.

1. X. Yang, Nature-inspired metaheuristic algorithms, Luniver Press, second edition. 2010.

2. D. Kumar, S. Kumar, R. Bansal, P. Singla, A survey to nature inspired soft computing, Int. J. Inf. Syst. Model., 8 (2017), 112–133. https://doi.org/10.4018/IJISMD.2017040107

3. A. Sharma, A. Sharma, B. K. Panigrahi, D. Kiran, R. Kumar, Ageist spider monkey optimization algorithm, Swarm Evol. Comput., 28 (2016), 58–77. https://doi.org/10.1016/j.swevo.2016.01.002.

4. J. C. Bansal, H. Sharma, S. S. Jadon, M. Clerc, Spider monkey optimization algorithm for numerical optimization, Memetic Comput., 6 (2014): 31–47. https://doi.org/10.1007/s12293-013-0128-0.

5. H. Sharma, G. Hazrati, J. C. Bansal, Spider monkey optimization algorithm, Evol. Swarm Intell. Algorithms, (2019), 43–59.