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

Dinesh Karunanidy; Rajakumar Ramalingam; Shakila Basheer; Nandhini Mahadevan; Mamoon Rashid; Dinesh Karunanidy; Rajakumar Ramalingam; Shakila Basheer; Nandhini Mahadevan; Mamoon Rashid

doi:10.3934/math.20231545

AIMS Mathematics

2023, Volume 8, Issue 12: 30244-30268. doi: 10.3934/math.20231545

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Research article

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

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

Received: 29 July 2023 Revised: 12 October 2023 Accepted: 17 October 2023 Published: 07 November 2023
MSC : 90C23, 90C29

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.
- multi-objective optimization,
- Java Macaque algorithm,
- nature-inspired optimization,
- continuous optimization problem,
- discrete optimization problem
Citation: Dinesh Karunanidy, Rajakumar Ramalingam, Shakila Basheer, Nandhini Mahadevan, Mamoon Rashid. MOJMA: A novel multi-objective optimization algorithm based Java Macaque Behavior Model[J]. AIMS Mathematics, 2023, 8(12): 30244-30268. doi: 10.3934/math.20231545

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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.

References

[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 doi: 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.
[6]	C. A. G. Santos, P. K. M. M. Freire, S. K. Mishra, Cuckoo search via Lévy flights for optimization of a physically-based runoff-erosion model, J. Urban Environ. Eng., 6 (2012), 123–131. https://www.jstor.org/stable/26203380
[7]	S. Yılmaz, E. U Kuc¸ uksille, A new modification approach on bat algorithm for solving optimization problems, Appl. Soft Comput., 28 (2015), 259–275. https://doi.org/10.1016/j.asoc.2014.11.029.
[8]	X. S. Yang, Firefly algorithm, Engineering optimization, 2010,221–230.
[9]	J. Kennedy, Particle swarm optimization, In: Encyclopedia of machine learning, 760–766. Springer, 2011.
[10]	D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm, J. Global Optim., 39 (2007), 459–471. https://doi.org/10.1007/s10898-007-9149-x doi: 10.1007/s10898-007-9149-x
[11]	M. Dorigo, M. Birattari, T. Stutzle, Ant colony optimization, IEEE Comput. Intell. M., 1 (2006), 28–39. https://doi.org/10.1109/MCI.2006.329691.
[12]	R. Storn, K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11 (1997), 341–359. https://doi.org/10.1007/s11042-022-12409-x doi: 10.1007/s11042-022-12409-x
[13]	J. H. Holland, Adaptation in natural and artificial systems, Univ. Mich. Press. Ann Arbor, 1975.
[14]	F. S. Gharehchopogh, T. Ibrikci, An improved african vultures optimization algorithm using different fitness functions for multi-level thresholding image segmentation, Multimed. Tools Appl., (2023), 1–47. https://doi.org/10.1007/s11042-023-16300-1.
[15]	S. T. Shishavan, F. S. Gharehchopogh, An improved cuckoo search optimization algorithm with genetic algorithm for community detection in complex networks, Multimed. Tools Appl., 81 (2022), 25205–25231.
[16]	F. S. Gharehchopogh, Quantum-inspired metaheuristic algorithms: comprehensive survey and classification, Artif. Intell. Rev., 56 (2023), 5479–5543, 2023. https://doi.org/10.1007/s10462-022-10280-8.
[17]	A. Laith, M. Shehab, M. Alshinwan, S. Mirjalili, M. A. Elaziz, Ant lion optimizer: A comprehensive survey of its variants and applications, Arch. Comput. Method. Eng., 28 (2021), 1397–1416. https://doi.org/10.1007/s11831-020-09420-6.
[18]	X. Yang, J. Zou, S. Yang, J. Zheng, Y. Liu, A fuzzy decision variables framework for large-scale multiobjective optimization, IEEE T. Evolut. Comput., 27 (2021), 445–459. https://doi.org/10.1109/TEVC.2021.3118593 doi: 10.1109/TEVC.2021.3118593
[19]	A. M. Basset, R. Mohamed, M. Abouhawwash, Balanced multi-objective optimization algorithm using improvement-based reference points approach, Swarm Evol. Comput., 60 (2021), 100791. https://doi.org/10.1016/j.swevo.2020.100791 doi: 10.1016/j.swevo.2020.100791
[20]	M. A. Basset, R. Mohamed, S. Mirjalili, A novel whale optimization algorithm integrated with nelder–mead simplex for multi-objective optimization problems, Knowledge-Based Syst., 212 (2021), 106619. https://doi.org/10.1016/j.knosys.2020.106619 doi: 10.1016/j.knosys.2020.106619
[21]	B. Xu, G. Zhang, K. Li, B. Li, H. Chi, Y. Yao, et al., Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles, Protect. Contr. Mod. Pow., 7 (2022), 51. https://doi.org/10.1016/j.ins.2022.05.123.
[22]	F. S. Gharehchopogh, An improved harris hawks optimization algorithm with multistrategy for community detection in social network, J. Bionic Eng., 20 (2023), 1175–1197. https://doi.org/10.1007/s42235-022-00303-z doi: 10.1007/s42235-022-00303-z
[23]	H. Ma, S. Shen, M. Yu, Z. Yang, M. Fei, H. Zhou, Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey, Swarm Evol. Comput., 44 (2019), 365–387. https://doi.org/10.1016/j.swevo.2018.04.011 doi: 10.1016/j.swevo.2018.04.011
[24]	Z. Li, V. Tam, L. K. Yeung, An adaptive multi-population optimization algorithm for global continuous optimization, IEEE Access, 9 (2021), 19960–19989. https://doi.org/10.1109/ACCESS.2021.3054636 doi: 10.1109/ACCESS.2021.3054636
[25]	X. Peng, Z. Shi, Finding informative collaborators for cooperative co-evolutionary algorithms using a dynamic multi-population framework, In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2016, 1–6. https://doi.org/10.1109/SSCI.2016.7849958.
[26]	K. Dinesh, R. Ramalingam, A. Dumka, R. Singh, I. Alsukayti, D. Anand, et al., An intelligent optimized route-discovery model for IoT-based VANETs, Processes, 9 (2021), 2171. https://doi.org/10.3390/pr9122171.
[27]	D. Saravanan, R. Rajakumar, M. Sreedevi, K. Dinesh, S. V. Sudha, D. K. Anguraj, et al., Multi-objective swarm-based model for deploying virtual machines on cloud physical servers, Distrib. Parallel Dat., 41 (2023), 75–93. https://doi.org/10.1007/s10619-021-07341-2.
[28]	Y. Liu, Y. Shi, H. Chen, A. A. Heidari, W. Gui, M. Wang, et al., Chaos-assisted multi-population salp swarm algorithms: Framework and case studies, Expert Syst. Appl., 168 (2021), 114369. https://doi.org/10.1016/j.eswa.2020.114369.
[29]	H. Li, Q. Zhang, Multiobjective optimization problems with complicated pareto sets, moea/d and nsga-ii, IEEE T. Evol. Comput., 13 (2009), 284–302. https://doi.org/10.1109/TEVC.2008.925798.
[30]	D. Karunanidy, S. Ramalingam, A. Dumka, R. Singh, M. Rashid, A. Gehlot, et al., Jma: Nature-inspired java macaque algorithm for optimization problem, Mathematics, 10 (2022), 688. https://doi.org/10.3390/math10050688.
[31]	K. Dinesh, J. Amudhavel, R. Rajakumar, P. Dhavachelvan, R. Subramanian, A novel self-organisation model for improving the performance of permutation coded genetic algorithm, Int. J. Adv. Intell. Paradigms, 17 (2020), 299–322. https://doi.org/10.1504/IJAIP.2020.109512 doi: 10.1504/IJAIP.2020.109512
[32]	D. Kalyanmoy, D Saxena, Searching for pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems, In: Proceedings of the world congress on computational intelligence, (2006), 3352–3360.
[33]	B. Xu, D. Gong, Y. Zhang, S. Yang, L. Wang, Z. Fan, et al., Cooperative co-evolutionary algorithm for multi-objective optimization problems with changing decision variables, Inf. Sci., 607 (2022), 278–296.
[34]	H. Mohammadzadeh, F. S. Gharehchopogh, A multi-agent system based for solving high-dimensional optimization problems: a case study on email spam detection, Int. J. Commun. Syst., 34 (2021), 4670. https://doi.org/10.1002/dac.4670 doi: 10.1002/dac.4670
[35]	Y. Yuan, H. Xu, B. Wang, X. Yao, A new dominance relation-based evolutionary algorithm for many-objective optimization, IEEE T. Evol. Comput., 20 (2015), 16–37. https://doi.org/10.1109/TEVC.2015.2420112 doi: 10.1109/TEVC.2015.2420112
[36]	E. Zitzler, S. Kunzli, Indicator-based selection in multiobjective search, In: International Conference on Parallel Problem Solving from Nature, 832–842. Springer, 2004. https://doi.org/10.1007/978-3-540-30217-9_84.
[37]	Q. Zhang, H. Li, Moea/d: A multiobjective evolutionary algorithm based on decomposition, IEEE T. Evol. Comput., 11 (2007), 712–731. https://doi.org/10.1109/TEVC.2007.892759 doi: 10.1109/TEVC.2007.892759
[38]	D. Kalyanmoy, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: Nsga-ii, IEEE T. Evol. Comput., 6 (2002), 182–197. https://doi.org/10.1109/4235.996017 doi: 10.1109/4235.996017
[39]	A. Trivedi, D. Srinivasan, K. Sanyal, A. Ghosh, A survey of multiobjective evolutionary algorithms based on decomposition, IEEE T. Evol. Comput., 21 (2017), 440–462. https://doi.org/10.1109/TEVC.2016.2608507 doi: 10.1109/TEVC.2016.2608507
[40]	X. Cai, Z. Mei, Z. Fan, Q. Zhang, A constrained decomposition approach with grids for evolutionary multiobjective optimization, IEEE T. Evol. Comput., 22 (2017), 564–577. https://doi.org/10.1109/TEVC.2017.2744674 doi: 10.1109/TEVC.2017.2744674
[41]	R. Cheng, Y. Jin, M. Olhofer, B. Sendhoff, A reference vector guided evolutionary algorithm for many-objective optimization, IEEE T. Evol. Comput., 20 (2016), 773–791. https://doi.org/10.1109/TEVC.2016.2519378.
[42]	K. Deb, K. Miettinen, S. Chaudhuri, Toward an estimation of nadir objective vector using a hybrid of evolutionary and local search approaches, IEEE T. Evol. Comput., 14 (2010), 821–841. https://doi.org/10.1109/TEVC.2010.2041667.
[43]	X. Ma, Q. Zhang, J. Yang, Z. Zhu, On tchebycheff decomposition approaches for multi-objective evolutionary optimization, IEEE T. Evol. Comput., 2017. https://doi.org/10.1109/TEVC.2017.2704118 doi: 10.1109/TEVC.2017.2704118
[44]	G. Syswerda, Uniform crossover in genetic algorithms. In: Proceedings of the third international conference on Genetic algorithms, Morgan Kaufmann Publishers, 3 (1989), 2–9.
[45]	H. Ishibuchi, N. Akedo, Y. Nojima, Behavior of multiobjective evolutionary algorithms on many-objective knapsack problems, IEEE T. Evol. Comput., 19 (2015), 264–283. https://doi.org/10.1109/TEVC.2014.2315442 doi: 10.1109/TEVC.2014.2315442
[46]	K. Deb, Multi-objective genetic algorithms: Problem difficulties and construction of test problems, Evol. Comput., 7 (1999), 205–230. https://doi.org/10.1162/evco.1999.7.3.205.
[47]	J. J. Durillo, A. J Nebro, jmetal: A java framework for multi-objective optimization, Adv. Eng. Soft., 42 (2011), 760–771. https://doi.org/10.1016/j.advengsoft.2011.05.014.
[48]	Q. Lin, J. Chen, Z. H. Zhan, W. N. Chen, C. A. C. Coello, Y. Yin, et al., A hybrid evolutionary immune algorithm for multiobjective optimization problems, IEEE T. Evol. Comput., 20 (2016), 711–729. https://doi.org/10.1109/TEVC.2015.2512930.
[49]	H. Karshenas, R. Santana, C. Bielza, P. Larranaga, Multiobjective estimation of distribution algorithm based on joint modeling of objectives and variables, IEEE T. Evol. Comput., 18 (2014), 519–542. https://doi.org/10.1109/TEVC.2013.2281524 doi: 10.1109/TEVC.2013.2281524
[50]	Q. Zhang, A. Zhou, S. Zhao, P. N. Suganthan, W. Liu, S. Tiwari, Multiobjective optimization test instances for the cec 2009 special session and competition, University of Essex, Colchester, UK and Nanyang technological University, Singapore, special session on performance assessment of multi-objective optimization algorithms, technical report, 264, 2008.

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