[1] H. Shafiei, V. Rafe, and M. Amiri, “Optimization of vehicle routing based on the combination of ant colony and particle swarm algorithms with the heuristic function of the cosine of angles,” Soft Comput. J., vol. 12, no. 2, pp. 146-164, 2024, doi: 10.22052/scj.2023.248702.1118 [In Persian].
[2] S. Mirjalili, A.H. Gandomi, S.Z. Mirjalili, S. Saremi, H. Faris, and S.M. Mirjalili, “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems,” Adv. Eng. Soft., vol. 114, pp. 163-191, 2017, doi: 10.1016/j.advengsoft.2017.07.002.
[3] U. Yuzgec and M. Kusoglu, “Multi-objective harris hawks optimizer for multiobjective optimization problems,” BSEU J. Eng. Res. Technol., vol. 1, no. 1, pp. 31-41, 2020.
[4] B. Cao, M. Li, X. Liu, J. Zhao, W. Cao, and Z. Lv, “Many-objective deployment optimization for a drone-assisted camera network,” IEEE Trans. Network Sci. Eng., vol. 8, no. 4, pp. 2756-2764, 2021, doi: 10.1109/TNSE.2021.3057915.
[5] J. Branke, T. Kaubler, and H. Schmeck, “Guidance in evolutionary multi-objective optimization,” Adv. Eng. Soft., vol. 32, no. 6, pp. 499-507, 2001, doi: 10.1016/S0965-9978(00)00110-1.
[6] M. Zamzame, S. Sedighian Kashi, and A. Nikanjam, “Energy-aware evolutionary multi-objective refactoring for bad code smells correction of Android applications,” Soft Comput. J., vol. 12, no. 2, pp. 72-89, 2024, doi: 10.22052/scj.2023.246479.1074 [In Persian].
[7] C. Lin, F. Gao, and Y. Bai, “An intelligent sampling approach for metamodel-based multi-objective optimization with guidance of the adaptive weighted-sum method,” Struct. Multidiscip. Optim., vol. 57, pp. 1047-1060, 2018, doi: 10.1007/s00158-017-1793-2.
[8] J. Behnamian, M. Zandieh, and S. Fatemi Ghomi, “Bi-objective parallel machines scheduling with sequence-dependent setup times using hybrid metaheuristics and weighted min–max technique,” Soft Comput., vol. 15, pp. 1313-1331, 2011, doi: 10.1007/s00500-010-0673-0.
[9] R. W. Hanks, B.J. Lunday, and J.D. Weir, “Robust goal programming for multi-objective optimization of data-driven problems: A use case for the United States transportation command’s liner rate setting problem,” Omega, vol. 90, p. 101983, 2020, doi: 10.1016/j.omega.2018.10.013.
[10] L. Lai, L. Fiaschi, M. Cococcioni, and K. Deb, “Solving mixed pareto-lexicographic multiobjective optimization problems: the case of priority levels,” IEEE Trans. Evol. Comput., vol. 25, no. 5, pp. 971-985, 2021, doi: 10.1109/TEVC.2021.3068816.
[11] J. Zheng, Z. Zhang, J. Zou, S. Yang, J. Ou, and Y. Hu, “A dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution,” Swarm Evol. Comput., vol. 69, p. 100987, 2022, doi: 10.1016/j.swevo.2021.100987.
[12] S. Mirjalili, S. Saremi, S. M. Mirjalili, and L.d.S. Coelho, “Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization,” Expert Syst. Appl., vol. 47, pp. 106-119, 2016, doi: 10.1016/j.eswa.2015.10.039.
[13] N. Srinivas and K. Deb, “Muiltiobjective optimization using nondominated sorting in genetic algorithms,” Evol. Comput., vol. 2, no. 3, pp. 221-248, 1994, doi: 10.1162/evco.1994.2.3.221.
[14] C.C. Coello and M.S. Lechuga, “MOPSO: A proposal for multiple objective particle swarm optimization,” in Proc. Cong. Evol. Comput. (CEC), 2002, vol. 2, pp. 1051-1056, doi: 10.1109/CEC.2002.1004388. 
[15] J. Salimisartaghti, and S. Goli Bidgoli, “A Hybrid Algorithm using Firefly, Genetic, and Local Search Algorithms,” Soft Comput. J., vol. 8, no. 1, pp. 14-28, 2019, doi: 10.22052/8.1.14 [In Persian].
[16] T. Murata and H. Ishibuchi, “MOGA: multi-objective genetic algorithms,” in IEEE Int. Conf. Evol. Comput., NJ, USA, 1995, vol. 1, pp. 289-294. 
[17] K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II,” in Parallel Problem Solving from Nature PPSN VI: 6th Int. Conf., Paris, France, 2000, Springer, pp. 849-858, doi: 10.1007/3-540-45356-3_83.
[18] J. Horn, N. Nafpliotis, and D.E. Goldberg, “A niched Pareto genetic algorithm for multiobjective optimization,” in Proc. 1st IEEE Conf. Evol. Comput., 1994, pp. 82-87, doi: 10.1109/ICEC.1994.350037.
[19] E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: Improving the strength Pareto evolutionary algorithm,” TIK Report, vol. 103, 2001, doi: 10.3929/ethz-a-004284029.
[20] A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, “Harris hawks optimization: Algorithm and applications,” Future Gener. Comput. Syst., vol. 97, pp. 849-872, 2019, doi: 10.1016/j.future.2019.02.028.
[21] H.A. Abbass, R. Sarker, and C. Newton, “PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems,” in Proc. 2001 Cong. Evol. Comput. (IEEE Cat. No. 01TH8546), 2001, vol. 2, pp. 971-978, doi: 10.1109/CEC.2001.934295. 
[22] S.-Y. Zeng et al., “A dynamic multi-objective evolutionary algorithm based on an orthogonal design,” in 2006 IEEE Int. Conf. Evol. Comput., 2006, pp. 573-580, doi: 10.1109/CEC.2006.1688361. 
[23] K. Zhong, G. Zhou, W. Deng, Y. Zhou, and Q. Luo, “MOMPA: Multi-objective marine predator algorithm,” Comput. Methods Appl. Mech. Eng., vol. 385, p. 114029, 2021, doi: 10.1016/j.cma.2021.114029
[24] S. Mirjalili, P. Jangir, and S. Saremi, “Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems,” Appl. Intell., vol. 46, pp. 79-95, 2017, doi: 10.1007/s10489-016-0825-8.
[25] N. Khodadadi, S.M. Mirjalili, W. Zhao, Z. Zhang, L. Wang, and S. Mirjalili, “Multi-objective artificial hummingbird algorithm,” in Adv. Swarm Intell. Variations Adapt. Optim. Probl., Springer, 2022, pp. 407-41, doi: 10.1007/978-3-031-09835-2_22
[26] A. Sadollah, H. Eskandar, A. Bahreininejad, and J.H. Kim, “Water cycle algorithm for solving multi-objective optimization problems,” Soft Comput., vol. 19, pp. 2587-2603, 2015, doi: 10.1007/s00500-014-1424-4.
[27] H.R. Hassanzadeh and M. Rouhani, “A multi-objective gravitational search algorithm,” in 2nd Int. Conf. Comput. Intell. Commun. Syst. Networks, 2010, pp. 7-12, doi: 10.1109/CICSyN.2010.32.
[28] J.J. Flores, R. Lopez, and J. Barrera, “Gravitational interactions optimization,” in Int. Conf. Learn. Intell. Optim., 2011, pp. 226-237, doi: 10.1007/978-3-642-25566-3_17. 
[29] M. Premkumar, P. Jangir, and R. Sowmya, “MOGBO: A new Multiobjective Gradient-Based Optimizer for real-world structural optimization problems,” Knowl.-Based Syst., vol. 218, p. 106856, 2021, doi: 10.1016/j.knosys.2021.106856.
[30] M. Premkumar, P. Jangir, R. Sowmya, H.H. Alhelou, S. Mirjalili, and B.S. Kumar, “Multi-objective equilibrium optimizer: Framework and development for solving multi-objective optimization problems,” J. Comput. Des. Eng., vol. 9, no. 1, pp. 24-50, 2022, doi: 10.1093/jcde/qwab065.
[31] F. Cao, Z. Tang, C. Zhu, and X. Zhao, “An Efficient Hybrid Multi-Objective Optimization Method Coupling Global Evolutionary and Local Gradient Searches for Solving Aerodynamic Optimization Problems,” Mathematics, vol. 11, no. 18, p. 3844, 2023, doi: 10.3390/math11183844.
[32] N. Khodadadi, M. Azizi, S. Talatahari, and P. Sareh, “Multi-objective crystal structure algorithm (MOCryStAl): Introduction and performance evaluation,” IEEE Access, vol. 9, pp. 117795-117812, 2021, doi: 10.1109/ACCESS.2021.3106487.
[33] S. Yacoubi, G. Manita, H. Amdouni, S. Mirjalili, and O. Korbaa, “A modified multi-objective slime mould algorithm with orthogonal learning for numerical association rules mining,” Neural Comput. Appl., vol. 35, no. 8, pp. 6125-6151, 2023, doi: 10.1007/s00521-022-07985-w.
[34] F. Zou, L. Wang, X. Hei, D. Chen, and B. Wang, “Multi-objective optimization using teaching-learning-based optimization algorithm,” Eng. Appl. Artif. Intell., vol. 26, no. 4, pp. 1291-1300, 2013, doi: 10.1016/j.engappai.2012.11.006.
[35] R.V. Rao, V.J. Savsani, and D. Vakharia, “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems,” Comput.-Aided Des., vol. 43, no. 3, pp. 303-315, 2011, doi: 10.1016/j.cad.2010.12.015.
[36] D.H. Wolpert and W.G. Macready, “No free lunch theorems for optimization,” IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 67-82, 1997, doi: 10.1109/4235.585893.
[37] E. Pira, “City councils evolution: A socio-inspired metaheuristic optimization algorithm,” J. Ambient Intell. Humaniz. Comput., pp. 1-50, 2022, doi: 10.1007/s12652-022-03765-5.
[38] J. Cao, J. Zhang, F. Zhao, and Z. Chen, “A two-stage evolutionary strategy based MOEA/D to multi-objective problems,” Expert Syst. Appl., vol. 185, p. 115654, 2021, doi: 10.1016/j.eswa.2021.115654.
[39] D.A. Van Veldhuizen and G.B. Lamont, “Multiobjective evolutionary algorithm research: A history and analysis,” Citeseer, 1998. 
[40] M.R. Sierra and C.A. Coello Coello, “Improving PSO-based multi-objective optimization using crowding, mutation and ∈-dominance,” in 3rd Int. Conf. Evol. Multi-Criterion Optim. (EMO), Guanajuato, Mexico, 2005, Springer, pp. 505-519, doi: 10.1007/978-3-540-31880-4_35.
[41] M. Friedman, “A comparison of alternative tests of significance for the problem of m rankings,” Ann. Math. Statist., vol. 11, no. 1, pp. 86-92, 1940.
[42] R.F. Woolson, “Wilcoxon signed‐rank test,” Wiley encyclopedia of clinical trials, pp. 1-3, 2007, doi: 10.1002/9780471462422.eoct979.