A Hybrid Multi-Objective Firefly-Sine Cosine Algorithm for Multi-Objective Optimization Problem
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A Hybrid Multi-Objective Firefly-Sine Cosine Algorithm for Multi-Objective Optimization Problem

Authors: Gaohuizi Guo, Ning Zhang


Firefly algorithm (FA) and Sine Cosine algorithm (SCA) are two very popular and advanced metaheuristic algorithms. However, these algorithms applied to multi-objective optimization problems have some shortcomings, respectively, such as premature convergence and limited exploration capability. Combining the privileges of FA and SCA while avoiding their deficiencies may improve the accuracy and efficiency of the algorithm. This paper proposes a hybridization of FA and SCA algorithms, named multi-objective firefly-sine cosine algorithm (MFA-SCA), to develop a more efficient meta-heuristic algorithm than FA and SCA.

Keywords: Firefly algorithm, hybrid algorithm, multi-objective optimization, Sine Cosine algorithm.

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[1] J. Kennedy, R. Eberhart, “Particle Swarm Optimization,” in: Neural Networks, 1995. Proceedings, IEEE International Conference on, volume 4, IEEE, 1995, pp. 1942–1948.
[2] M. Dorigo, G. Di Caro, “Ant colony optimization: a new meta-heuristic,” in: Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, vol. 2, IEEE, 1999.
[3] D. Karaboga, “An Idea Based on Honey Bee Swarm for Numerical Optimization.” Techn. Rep. TR06, Erciyes Univ. Press, Erciyes, 2005.
[4] X.S. Yang, Nature-inspired metaheuristic algorithms, Luniver Press, Beckington, 2008.
[5] S. Mirjalili, “SCA: A sine cosine algorithm for solving optimization problems,” Knowledge-Based Systems, vol. 96, pp. 120–133, 2016.
[6] X. S. Yang, “Multiobjective firefly algorithm for continuous optimization,” Engineering with Computers, vol. 29, no. 2, pp. 175–184, 2013.
[7] M. K. Marichelvam, T. Prabaharan, and X. S. Yang, “A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problems,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 2, pp. 301–305, 2014.
[8] S. Karthikeyan, P. Asokan, S. Nickolas, and T. Page, “A hybrid discrete firefly algorithm for solving multi-objective flexible job shop scheduling problems,” International Journal of Bio-Inspired Computation, vol. 7, no. 6, pp. 386–401, 2015.
[9] A. Hidalgo-Paniagua, M. A. Vega-Rodríguez, J. Ferruz, and N. Pavón, “Solving the multiobjective path planning problem in mobile robotics with a firefly-based approach,” Soft Computing, vol. 21, pp. 949–964, 2017.
[10] O. Bozorg-Haddad, I. Garousi-Nejad, and H. A. Loáiciga, “Extended multi-objective firefly algorithm for hydropower energy generation,” Journal of Hydroinformatics, vol. 19, no. 5, pp. 734–751, 2017.
[11] S. Lu, N. Zhang, Y. Qiu, and Y. Gao, “A multi-period regret minimization model for uncertain portfolio selection with bankruptcy constraint,” Journal of Intelligent and Fuzzy Systems, vol. 37, no. 6, pp. 8417–8439, 2019.
[12] Y. Aref and K. Cemal, “A modified firefly algorithm for global minimum optimization,” Applied Soft Computing, vol. 62, pp. 29–44, 2018.
[13] J. Zhang, Y.F. Teng and W. Chen, “Support vector regression with modified firefly algorithm for stock price forecasting,” Applied Intelligence, vol. 49, pp. 1658–1674, 2019.
[14] D. A. Van, V. Gary and B. Lamont. “Multiobjective evolutionary algorithm research: Ahistory and analysis, ” Evolutionary Computation, vol. 8, pp. 125–147, 1998.
[15] H. Xing, Z. Wang, T. Li, H. Li and R. Qu. “An improved MOEA/D algorithm for multi-objective multicast routing with network coding,” Applied Soft Computing, vol. 59, pp. 88–103, 2017.
[16] J. R. Schott, “Fault tolerant design using single and multicriteria genetic algorithm optimization,” Cellular Immunology, vol. 37, pp. 1–13, 1995.