DOI QR코드

DOI QR Code

PSA: A Photon Search Algorithm

  • Liu, Yongli (School of Computer Science and Technology, Henan Polytechnic University) ;
  • Li, Renjie (School of Computer Science and Technology, Henan Polytechnic University)
  • Received : 2018.12.06
  • Accepted : 2019.09.26
  • Published : 2020.04.30

Abstract

We designed a new meta-heuristic algorithm named Photon Search Algorithm (PSA) in this paper, which is motivated by photon properties in the field of physics. The physical knowledge involved in this paper includes three main concepts: Principle of Constancy of Light Velocity, Uncertainty Principle and Pauli Exclusion Principle. Based on these physical knowledges, we developed mathematical formulations and models of the proposed algorithm. Moreover, in order to confirm the convergence capability of the algorithm proposed, we compared it with 7 unimodal benchmark functions and 23 multimodal benchmark functions. Experimental results indicate that PSA has better global convergence and higher searching efficiency. Although the performance of the algorithm in solving the optimal solution of certain functions is slightly inferior to that of the existing heuristic algorithm, it is better than the existing algorithm in solving most functions. On balance, PSA has relatively better convergence performance than the existing metaheuristic algorithms.

Keywords

References

  1. J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, 1995, pp. 1942-1948.
  2. D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm," Journal of Global Optimization, vol. 39, no. 3, pp. 459-471, 2007. https://doi.org/10.1007/s10898-007-9149-x
  3. S. Mirjalili and A. Lewis, "The whale optimization algorithm," Advances in Engineering Software, vol. 95, pp. 51-67, 2016. https://doi.org/10.1016/j.advengsoft.2016.01.008
  4. M. Dorigo, M. Birattari, and T. Stutzle, "Ant colony optimization," IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28-39, 2006. https://doi.org/10.1109/MCI.2006.329691
  5. P. C. Pinto, T. A. Runkler, and J. M. Sousa, "Wasp swarm algorithm for dynamic MAX-SAT problems," in Adaptive and Natural Computing Algorithms. Heidelberg: Springer, 2007, pp. 350-357.
  6. A. Mucherino and O. Seref, "Monkey search: a novel metaheuristic search for global optimization," AIP Conference Proceedings, vol. 953, no. 1, pp. 162-173, 2007.
  7. A. Sharma, A. Sharma, B. K. Panigrahi, D. Kiran, and R. Kumar, "Ageist spider monkey optimization algorithm," Swarm and Evolutionary Computation, vol. 28, pp. 58-77, 2016. https://doi.org/10.1016/j.swevo.2016.01.002
  8. Q. Zhou and Y. Q. Zhou, "Wolf colony search algorithm based on leader strategy," Application Research of Computers, vol. 30, no. 9, pp. 2629-2632, 2013. https://doi.org/10.3969/j.issn.1001-3695.2013.09.018
  9. C. R. Hwang, "Simulated Annealing: Theory and Applications by P. J. M. Van Laarhoven and E. H. Aarts, 1987," Acta Applicandae Mathematica, vol. 12, pp. 108-111, 1988. https://doi.org/10.1007/bf00047572
  10. E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: a gravitational search algorithm," Information Sciences, vol. 179, no. 13, pp. 2232-2248, 2009. https://doi.org/10.1016/j.ins.2009.03.004
  11. H. Shah-Hosseini, "Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimization," International Journal of Computational Science and Engineering, vol. 6, no. 1-2, pp. 132-140, 2011. https://doi.org/10.1504/ijcse.2011.041221
  12. A. Kaveh and S. Talatahari, "A novel heuristic optimization method: charged system search," Acta Mechanica, vol. 213, no. 3-4, pp. 267-289, 2010. https://doi.org/10.1007/s00707-009-0270-4
  13. R. A. Formato, "Central force optimization: a new deterministic gradient-like optimization metaheuristic," Opsearch, vol. 46, no. 1, pp. 25-51, 2009. https://doi.org/10.1007/s12597-009-0003-4
  14. A. Hatamlou, "Black hole: a new heuristic optimization approach for data clustering," Information Sciences, vol. 222, pp. 175-184, 2013. https://doi.org/10.1016/j.ins.2012.08.023
  15. H. Huang, M. Zhu, and J. Wang, "An improved artificial bee colony algorithm based on special division and intellective search," Journal of Information Processing Systems, vol. 15, no. 2, pp. 433-439, 2019. https://doi.org/10.3745/JIPS.02.0111
  16. L. Zhao and Y. Long, "An improved PSO algorithm for the classification of multiple power quality disturbances," Journal of Information Processing Systems, vol. 15, no. 1, pp. 116-126, 2019. https://doi.org/10.3745/JIPS.04.0102
  17. X. Song, M. Zhao, Q. Yan, and S. Xing, "A high-efficiency adaptive artificial bee colony algorithm using two strategies for continuous optimization," Swarm and Evolutionary Computation, vol. 50, article no. 100549, 2019. https://doi.org/10.1016/j.swevo.2019.01.009
  18. M. R. Chen, J. H. Chen, G. Q. Zeng, K. D. Lu, and X. F. Jiang, "An improved artificial bee colony algorithm combined with extremal optimization and Boltzmann Selection probability," Swarm and Evolutionary Computation, vol. 49, pp. 158-177, 2019. https://doi.org/10.1016/j.swevo.2019.06.005
  19. M. Li, D. Lei, and J. Cai, "Two-level imperialist competitive algorithm for energy-efficient hybrid flow shop scheduling problem with relative importance of objectives," Swarm and Evolutionary Computation, vol. 49, pp. 34-43, 2019. https://doi.org/10.1016/j.swevo.2019.05.006
  20. H. Kragh, "Max Planck: the reluctant revolutionary," Physics World, vol. 13, no. 12, pp. 31-36, 2000. https://doi.org/10.1088/2058-7058/13/12/34
  21. A. Einstein, "Uber einen die erzeugung und verwandlung des lichtes betreffenden heuristischen gesichtspunkt," Annalen der Physik, vol. 322, no. 6, pp. 132-148, 1905. https://doi.org/10.1002/andp.19053220607
  22. L. V. de Broglie, "On the theory of quanta," Annales de Physique, vol. 10, no. 3, pp. 22-128, 1925. https://doi.org/10.1051/anphys/192510030022