Abstract
In this paper, an improved multi-objective ABC algorithm based on k-means clustering, called CMOABC, is proposed. For keeping the population diversity, the multi-swarm technology based on k-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iterations, the population will be re-clustered to facilitate information exchange among different clusters. CMOABC is applied to solve the real-world Optimal Power Flow (OPF) problem that considers the cost, loss, and emission impacts as the objective functions. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.
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References
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Coello, C.A.C., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 256–279 (2004)
Zhang, Q., Liu, W., Li, H.: The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances. In: Proceedings of the Congress on Evolutionary Computation (CEC 2009), Norway, pp. 203–208 (2009)
Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214, 108–132 (2009)
Omkar, S.N., Senthilnath, J., Khandelwal, R., Naik, G.N., Gopalakrishnan, S.: Artificial bee colony (ABC) for multi-objective design optimization of composite structures. Appl. Soft Comput. 11(1), 489–499 (2011)
Sivasubramani, S., Swarup, K.S.: Environmental/economic dispatch using multi-objective harmony search algorithm. Electr. Power Syst. Res. 81, 1778–1785 (2011)
El-Keib, A., Ma, H., Hart, J.: Economic dispatch in view of the clean air act of 1990. IEEE Trans. Power Syst. 9(2), 972–978 (1994)
Halder, U., Das, S., Maity, D.: A cluster-based differential evolution algorithm with external archive for optimization in dynamic environments. IEEE Trans. Cybern. 43(3), 881–897 (2013)
Song, T., Pan, Z., Wong, D.M., Wang, X.: Design of logic gates using spiking neural P systems with homogeneous neurons and astrocytes-like control. Inf. Sci. 372, 380–391 (2016)
Wang, X., Song, T., Gong, F., Pan, Z.: On the computational power of spiking neural P systems with self-organization. Sci. Rep. (2016). doi:10.1038/srep27624
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Sun, L., Chen, H. (2016). Artificial Bee Colony Algorithm Based on Clustering Method and Its Application for Optimal Power Flow Problem. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_13
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DOI: https://doi.org/10.1007/978-981-10-3614-9_13
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