Abstract
In this paper, the two hybrid swarm-based metaheuristic algorithms are tested and compared. The first hybrid is already existing Firefly Particle Swarm Optimization (FFPSO), which is based, as the name suggests, on Firefly Algorithm (FA) and Particle Swarm Optimization (PSO). The secondly proposed hybrid is an algorithm using the multi-swarm method to merge FA and PSO. The performance of our developed algorithm is tested and compared with the FFPSO and canonical FA. Comparisons have been conducted on five selected benchmark functions, and the results have been evaluated for statistical significance using Friedman rank test.
T. Kadavy—This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014), further by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2018/003. This work is also based upon support by COST (European Cooperation in Science & Technology) under Action CA15140, Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO), and Action IC1406, High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). The work was further supported by resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz).
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References
Pluhacek, M., Senkerik, R., Viktorin, A., Kadavy, T., Zelinka, I.: A review of real-world applications of particle swarm optimization algorithm. In: Duy, V., Dao, T., Zelinka, I., Kim, S., Phuong, T. (eds.) AETA 2017. LNEE. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-69814-4_112018
Du, W., Li, B.: Multi-strategy ensemble particle swarm optimization for dynamic optimization. Inf. Sci. 178(15), 3096–3109 (2008)
Wang, H., Wu, Z., Rahnamayan, S., Sun, H., Liu, Y., Pan, J.: Multi-strategy ensemble artificial bee colony algorithm. Inf. Sci. 20(279), 587–603 (2014)
Blackwell, T., Branke, J.: Multi-swarm optimization in dynamic environments. In: Raidl, G.R., et al. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 489–500. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24653-4_50
Liang, J.J., Suganthan, P.N.: Dynamic multi-swarm particle swarm optimizer with local search. IEEE (2005)
Lynn, N., Suganthan, P.N.: Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol. Comput. 1(24), 11–24 (2015)
Nepomuceno, F.V., Engelbrecht, A.P.: A self-adaptive heterogeneous PSO for real-parameter optimization. IEEE (2013)
Zhan, Z.-H., Zhang, J., Li, Y., Shi, Y.-H.: Orthogonal learning particle swarm optimization. TEVC 15(6), 832–847 (2011)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, pp. 39–43. IEEE (1995)
Allahverdi, A., Al-Anzi, F.S.: A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application. Comput. Oper. Res. 33(4), 1056–1080 (2006)
Assareh, E., Behrang, M.A., Assari, M.R., Ghanbarzadeh, A.: Application of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) techniques on demand estimation of oil in Iran. Energy 35(12), 5223–5229 (2010)
Rudek, M., Canciglieri Jr., O., Greboge, T.: A PSO application in skull prosthesis modelling by superellipse. ELCVIA Electron. Lett. Comput. Vis. Image Anal. 12(2), 1–12 (2013). https://doi.org/10.5565/rev/elcvia.514
Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Frome (2010)
Gandomi, A.H., Yang, X.S., Talatahari, S., Alavi, A.H.: Firefly algorithm with chaos. Commun. Nonlinear Sci. Numer. Simul. 18(1), 89–98 (2013)
Yang, X.: Firefly algorithm, Lévy flights and global optimization. In: Bramer, M., Ellis, R., Petridis, M. (eds.) Research and Development in Intelligent Systems XXVI, pp. 209–218. Springer, London (2010). https://doi.org/10.1007/978-1-84882-983-1_15
Farahani, S.M., Abshouri, A.A., Nasiri, B., Meybodi, M.R.: A Gaussian firefly algorithm. Int. J. Mach. Learn. Comput. 1(5), 448 (2011)
Kora, P., Rama Krishna, K.S.: Hybrid firefly and particle swarm optimization algorithm for the detection of bundle branch block. Int. J. Cardiovasc. Acad. 2(1), 44–48 (2016)
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Kadavy, T., Pluhacek, M., Viktorin, A., Senkerik, R. (2018). Multi-swarm Optimization Algorithm Based on Firefly and Particle Swarm Optimization Techniques. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_38
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