References

  1. M.H. Davijani, M.E. Banihabib, A.N. Anvar, S.R. Hashemi, Optimization model for the allocation of water resources based on the maximization of employment in the agriculture and industry sectors, J. Hydrol., 533 (2016) 430–438.
  2. S. Yu, H. Lu, An integrated model of water resources optimization allocation based on projection pursuit model-Grey wolf optimization method in a transboundary river basin, J. Hydrol., 559 (2018)156–165.
  3. C.S. Orloff, A fundamental problem in vehicle routing, Networks, 4 (1974) 35–64.
  4. N. Christofides, V. Campos, A. Corbern, An Algorithm for the Rural Postman Problem on a Directed Graph/Netflow at Pisa, Springer Berlin, Heidelberg, 1986, pp. 155–166.
  5. A. Corberán, J.M. Sanchis, A polyhedral approach to the rural postman problem, Eur. J. Oper. Res., 79 (1994) 95–114.
  6. P.F. de Córdoba, L.M.G Raffi, J.M. Sanchis, A heuristic algorithm based on Monte Carlo methods for the rural postman problem, Comput. Oper. Res., 25 (1998) 1097–1106.
  7. G. Ghiani, G. Laporte, A branch-and-cut algorithm for the undirected rural postman problem, Math. Program., 87 (2000) 467–481.
  8. G. Ghiani, L. Demetrio, R. Musmanno, A constructive heuristic for the undirected rural postman problem, Comput. Oper. Res., 33 (2006) 3450–3457.
  9. G.W. Groves, J.H. Van Vuuren, Efficient heuristics for the rural postman problem, Orion, 21 (2005) 33–51.
  10. M.L. Pérez-Delgado, A Solution to the Rural Postman Problem Based on Artificial Ant Colonies, Conference of the Spanish Association for Artificial Intelligence, Springer, Berlin, Heidelberg, 2007, pp. 220–228.
  11. C. Archetti, G. Guastaroba, M.G. Speranza, Reoptimizing the rural postman problem, Comput. Oper. Res., 40 (2013) 1306–1313.
  12. M.R Garey, D.S. Johnson, Computers and Intractability, Freeman, San Francisco, 1979.
  13. R.P. Feynman, In: D.H. Gilbert, ed., Minaturization, Reinhold, New York, 1961, pp. 282–296.
  14. L.M. Adleman, Molecular computation of solution to combinatorial problems, Science, 266 (1994) 1021–1024.
  15. R.J. Lipton, DNA solution of HARD computational problems, Science, 268 (1995) 542–545.
  16. E. Winfree, F. Liu, L.A. Wenzler, N.C. Seeman, Design and selfassembly of two dimensional DNA crystals, Nature, 394 (1998) 539–544.
  17. Q. Ouyang, P.D. Kaplan, S. Liu, A. Libchaber, DNA solution of the maximal clique problem, Science, 278 (1997) 446–449.
  18. S. Roweis, E. Winfree, R. Burgoyne, N.V. Chelyapov, M.F. Goodman, P.W.K. Rothemund, L.M. Adleman, A sticker based model for DNA computation, J. Comput. Biol., 5 (1998) 615–629.
  19. D.M. Xiao, W.X. Li, Z.Z. Zhang, L. He, Solving maximum cut problems in the Adleman-Lipton model, BioSystems, 82 (2005) 203–207.
  20. K. Sakamoto, H. Gouzu, K. Komiya, D. Kiga, S. Yokoyama, T. Yokomori, M. Hagiya, Molecular computation by DNA hairpin formation, Science, 288 (2000) 1223–1226.
  21. W.X. Li, D.M. Xiao, L. He, DNA ternary addition, Appl. Math. Comput., 182 (2006) 977–986.
  22. D.M. Xiao, W.X. Li, J. Yu, X.D. Zhang, Z.Z. Zhang, L. He, Procedures for a dynamical system on {0,1}n with DNA molecules, BioSystems, 84 (2006) 207–216.
  23. W.X. Li, E.M. Patrikeev, D.M. Xiao, A DNA Algorithm for the maximal matching problem, Autom. Remote Control, 76 (2015) 1797–1802.
  24. Z. Wang, D. Huang, H. Meng, C. Tang, A new fast algorithm for solving the minimum spanning tree problem based on DNA molecules computation, Biosystems, 114 (2013) 1–7.
  25. M.Y. Guo, W.L. Chang, M. Ho, J. Lu, J.N. Cao, Is optimal solution of every NP-complete or NP-hard problem determined from its characteristic for DNA-based computing, BioSystems, 80 (2005) 71–82.
  26. W.-L. Chang, K.W. Lin, J.-C. Chen, C.-C. Wang, L.C. Lu, M. Guo, M. Ho, Molecular Solutions of the RSA Public-key Cryptosystem on a DNA-based Computer, J. Supercomput., 61 (2012) 642–672.
  27. W.-L. Chang, T.-T. Ren, J. Luo, M. Feng, M. Guo, Quantum algorithms for bio-molecular solutions of the satisfiability problem on a quantum machine, IEEE Trans. Nanobiosci., 7 (2008) 215–222.
  28. Z.C. Wang, J. Tan, D.M. Huang, Y. Ren, Z. Ji, A biological algorithm to solve the assignment problem based on DNA molecules computation, Appl. Math. Comput., 244 (2014) 183–190.
  29. Z. Wang, Z. Ji, X. Wang, T. Wu, W. Huang, A new parallel DNA algorithm to solve the task scheduling problem based on inspired computational model, BioSystems, 162 (2017) 59–65.
  30. X.C. Liu, X.F. Yang, S.L. Li, Y. Ding, Solving the minimum bisection problem using a biologically inspired computational model, Theor. Comput. Sci., 411 (2010) 888–896.
  31. Z. Wang, J. Pu, L. Cao, J. Tan, A parallel biological optimization algorithm to solve the unbalanced assignment problem based on DNA molecular computing, Int. J. Mol. Sci., 16 (2015) 25338–25352.
  32. W.L. Chang, T.T. Ren, M. Feng, Quantum algorithms and mathematical formulations of biomolecular solutions of the vertex cover problem in the finite-dimensional hilbert space, IEEE Trans. Nanobiosci., 14 (2014) 121–128.
  33. H. Zhao, J. Zheng, J. Xu, W. Deng. Fault diagnosis method based on principal component analysis and broad learning system, IEEE Access, 7 (2019) 99263–99272.
  34. W. Deng, R. Yao, H. Zhao, X. Yang, G. Li, A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm, Soft Comput., 23 (2019) 2445–2462.
  35. W. Deng, H. Zhao, L. Zou, G. Li, X. Yang, D. Wu, A novel collaborative optimization algorithm in solving complex optimization problems, Soft Comput., 21 (2017) 4387–4398.
  36. Z. Ji, Z. Wang, X. Bao, X. Wang, T. Wu, Research on water resources optimal scheduling problem based on parallel biological computing, Desal. Water Treat., 111 (2018) 88–93.
  37. W. Deng, J. Xu, H. Zhao, An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem, IEEE Access, 7 (2019) 20281–20292.
  38. R.S. Braich, C. Johnson, P.W.K. Rothemund, N. Chelyapov, L.M. Adleman, Solution of a 20-variable 3-SAT problem on a DNA computer, Science, 296 (2002) 499–502.
  39. R.B.A. Bakar, J. Watada, W. Pedrycz, DNA approach to solve clustering problem based on a mutual order, Biosystems, 91 (2008) 1–12.
  40. K.H. Zimmermann, Z. Ignatova, I. Martínez-Pérez, DNA Computing Models, Springer-Verlag US, 2008, pp. 146–147.
  41. Z. Ji, Z. Wang, A. Deng, W. Huang, T. Wu, A new parallel algorithm to solve one classic water resources optimal allocation problem based on inspired computational model, Desal. Water Treat., 160 (2019) 214–218.
  42. Z. Wang, D. Huang, J. Tan, T. Liu, K. Zhao, L. Li, A parallel algorithm for solving the n-queens problem based on inspired computational model, Biosystems, 131 (2015) 22–29.
  43. H.Y. Zhang, X.Y. Liu, A CLIQUE algorithm using DNA computing techniques based on closed-circle DNA sequences, Biosystems, 105 (2011) 73–82.
  44. M. Yamamura, Y. Hiroto, T. Matoba, Solutions of shortest path problems by concentration control, Lect. Notes Comput. Sci., 2340 (2002) 231–240.
  45. R.S. Braich, C. Johnson, P.W.K. Rothemund, D. Hwang, N. Chelyapov, L.M. Adleman, Solution of a Satisfiability Problem on a Gel-based DNA Computer, International Workshop on DNA-Based Computers, Springer, Berlin, Heidelberg, 2000, pp. 27–42.
  46. Z. Wang, X. Ren, Z. Ji, W. Huang, T. Wu, A novel bio-heuristic computing algorithm to solve the capacitated vehicle routing problem based on Adleman–Lipton model, Biosystems, 184 (2019) 103997.