Flower Pollination Algorithm Combined with Multiple Strategies of Opposition–Based Learning

  • Cácio L. N. A. Bezerra IFRN
  • Fábio G. B. C. Costa IFRN
  • Lucas V. Bazante IFRN
  • Pedro V. M. Carvalho IFRN
  • Fábio A. P. Paiva IFRN

Resumo


Flower Pollination Algorithm (FPA) has been widely used to solve optimization problems. However, it faces the problem of stagnation in local optimum. Several approaches have been proposed to deal with this problem. To improve the performance of the FPA, this paper presents a new variant that combines FPA and two variants of the Opposition Based Learning (OBL), such as Quasi OBL (QOBL) and Elite OBL (EOBL). To evaluate this proposal, 10 benchmark functions were used. In addition, the proposed algorithm was compared with original FPA and three variants such as FA–EOBL, SBFPA and DE–FPA. The proposal presented significant results.

Referências


Chakraborty, D., Saha, S., and Dutta, O. (2014). De-fpa: A hybrid differential evolutionflower pollination algorithm for function minimization. In International Conference on High Performance Computing and Applications (ICHPCA), pages 1–6. IEEE.

Hezam, I. M., Abdel-Baset, M., and Hassan, B. M. (2016). A hybrid flower pollination algorithm with tabu search for unconstrained optimization problems. Inf. Sci. Lett, 5:29–34.

Kalra, S. and Arora, S. (2016). Firefly algorithm hybridized with flower pollination algorithm for multimodal functions. In Proceedings of the International Congress on Information and Communication Technology, pages 207–219. Springer.

Leite, I. V., Marcone, M. H., and Paiva, F. A. (2018). Meta-heurística inspirada na bioluminescência dos vaga-lumes usando aprendizagem baseada em oposição elite. Anais do Computer on the Beach, pages 880–889.

Mantegna, R. N. (1994). Fast, accurate algorithm for numerical simulation of levy stable stochastic processes. Physical Review E, 49(5):4677.

Meng, O. K., Pauline, O., Kiong, S. C.,Wahab, H. A., and Jafferi, N. (2017). Application of modified flower pollination algorithm on mechanical engineering design problem. In IOP Conference Series: Materials Science and Engineering, volume 165.

Paiva, F. A. P., Silva, C. R. M., Leite, I. V. O., Marcone, M. H., and Costa, J. A. F. (2017). Uma nova abordagem do conceito de serendipidade como base para a metaheurística inspirada no processo de polinização das flores. In XIII Congresso Brasileiro em Inteligência Computacional (CBIC2017).

Rahnamayan, S., Tizhoosh, H. R., and Salama, M. M. (2007). Quasi-oppositional differential evolution. In IEEE Congress on Evolutionary Computation, pages 2229–2236.

Ramadas, M. and Kumar, S. (2016). An efficient hybrid approach using differential evolution and flower pollination algorithm. In Cloud System and Big Data Engineering (Confluence), 2016 6th International Conference, pages 59–64.

Saxena, P. and Kothari, A. (2016). Linear antenna array optimization using flower pollination algorithm. SpringerPlus, 5(1):306.

Shareef, S. S., Mohideen, E. R., and Ali, L. (2015). Directed firefly algorithm for multimodal problems. In Conference on Computational Intelligence and Computing Research (ICCIC), pages 1–6.

Tizhoosh, H. R. (2005). Opposition-based learning: a new scheme for machine intelligence. In Int. Conf. on Computational intelligence for modelling, control and automation and Int. Conf. on intelligent agents, web technologies and internet commerce, volume 1, pages 695–701.

Tran, T., Nguyen, T. T., and Nguyen, H. L. (2014). Global optimization using l˜A c vy flights. arXiv preprint arXiv:1407.5739.

Yang, X.-S. (2012). Flower pollination algorithm for global optimization. In International Conference on Unconventional Computing and Natural Computation, pages 240–249. Springer.

Zhou, X., Wu, Z., and Wang, H. (2012). Elite opposition-based differential evolution for solving large-scale optimization problems and its implementation on gpu. In 13th Int. Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), pages 727–732.

Publicado
22/10/2018
BEZERRA, Cácio L. N. A.; COSTA, Fábio G. B. C.; BAZANTE, Lucas V.; CARVALHO, Pedro V. M.; PAIVA, Fábio A. P.. Flower Pollination Algorithm Combined with Multiple Strategies of Opposition–Based Learning. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 15. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 620-631. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2018.4453.