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access icon free Selecting a biodiversity conservation area with a limited budget using the binary African buffalo optimisation algorithm

The reserves or protected areas have a fundamental role in the biodiversity of the planet. The main objective of the reserves is to protect areas where a large number of animal and plant species coexist, considering also, a set of abiotic factors such as water, soil and sunlight. This research solves the budget-constrained maximal covering location (BCMCL) problem. The aim of BCMCL problem is to maximise the number of species to be protected by the constraints of a limited budget and the costs that have to protect each area. The BCMCL problem is an NP-hard optimisation problem with a binary domain. For the resolution of BCMCL problem, the authors propose a binary version of African buffalo optimisation (ABO). The tests performed to solve the BCMCL problem have used a set of 12 test instances that have been solved by the algorithm binary ABO. Moreover, eight transfer functions have been applied and experienced in the binary version of ABO. The algorithms migrating birds optimisation, random descent and steepest descent are used to compare the best results obtained by ABO. Finally, the results show that the binary version of ABO has competitive results compared with other algorithms.

References

    1. 1)
      • 21. Saremi, S., Mirjalili, S., Lewis, A.: ‘How important is a transfer function in discrete heuristic algorithms’, Neural Comput. Appl., 2015, 26, (3), pp. 625640.
    2. 2)
      • 15. Odili, J.B., Kahar, M.N.M., Anwar, S., et al: ‘A comparative study of African buffalo optimization and randomized insertion algorithm for asymmetric travelling salesman's problem’. 4th Int. Conf. on Software Engineering and Computer Systems (ICSECS), 2015, 2015, pp. 9095.
    3. 3)
      • 33. Hochberg, Y.: ‘A sharper Bonferroni procedure for multiple tests of significance’, Biometrika, 1988, 75, (4), pp. 800802.
    4. 4)
      • 25. Osaba, E., Diaz, F., Carballedo, R., et al: ‘Focusing on the golden ball metaheuristic: an extended study on a wider set of problems’, Sci. World J., 2014.
    5. 5)
      • 5. Wilson, K.A., Underwood, E.C., Morrison, S.A., et al: ‘Conserving biodiversity efficiently: What to do, where, and when, PLOS Biology 5’.
    6. 6)
      • 30. Hollander, M., Wolfe, D.A., Chicken, E.: ‘Nonparametric statistical methods’ (John Wiley & Sons, 2013).
    7. 7)
      • 13. Odili, J.B., Kahar, M.N.M.: ‘African buffalo optimization (abo): a new meta-heuristic algorithm’, J. Adv. Appl. Sci., 2015, 3, pp. 101106.
    8. 8)
      • 32. Gao, X., Alvo, M.: ‘Nonparametric multiple comparison procedures for unbalanced two-way layouts’, J. Stat. Plan. Inference, 2008, 138, (12), pp. 36743686.
    9. 9)
      • 17. Odili, J.B., Kahar, M.N., Noraziah, A.: ‘Solving traveling salesmans problem using African buffalo optimization, honey bee mating optimization & lin-kerninghan algorithms’, World Appl. Sci. J., 2016, 34, (7), pp. 911916.
    10. 10)
      • 28. Royston, P.: ‘Approximating the Shapiro–Wilk W-test for non-normality’, Stat. Comput., 1992, 2, (3), pp. 117119.
    11. 11)
      • 26. Royston, J.P.: ‘An extension of Shapiro and Wilk's W test for normality to large samples’, J. R. Stat. Soc. Ser. C, Appl. Stat., 1982, 31, (2), pp. 115124.
    12. 12)
      • 31. Gao, X., Alvo, M., Chen, J., et al: ‘Nonparametric multiple comparison procedures for unbalanced one-way factorial designs’, J. Stat. Plan. Inference, 2008, 138, (8), pp. 25742591.
    13. 13)
      • 9. Rosing, K., ReVelle, C., Williams, J.: ‘Maximizing species representation under limited resources: a new and efficient heuristic’, Environ. Model. Assess., 2002, 7, (2), pp. 9198.
    14. 14)
      • 11. Marianov, V., ReVelle, C., Snyder, S.: ‘Selecting compact habitat reserves for species with differential habitat size needs’, Comput. Oper. Res., 2008, 35, (2), pp. 475487.
    15. 15)
      • 20. Mirjalili, S., Hashim, S., Taherzadeh, G., et al: ‘A study of different transfer functions for binary version of particle swarm optimization’, GEM'11.
    16. 16)
      • 24. Almonacid, B.: ‘Dataset – selecting a biodiversity conservation area with a limited budget using the binary African buffalo optimization algorithm’. doi:10.6084/m9.figshare.5165443.v1.
    17. 17)
      • 6. Larson, E.R., Howell, S., Kareiva, P., et al: ‘Constraints of philanthropy on determining the distribution of biodiversity conservation funding’, Conserv. Biol., 2016, 30, (1), pp. 206215.
    18. 18)
      • 22. Duman, E., Uysal, M., Alkaya, A.F.: ‘Migrating birds optimization: a new metaheuristic approach and its performance on quadratic assignment problem’, Inf. Sci., 2012, 217, pp. 6577.
    19. 19)
      • 14. Odili, J.B., Kahar, M.N.M.: ‘Numerical function optimization solutions using the African buffalo optimization algorithm (ABO)’, Br. J. Math. Comput. Sci., 2015, 10, pp. 112.
    20. 20)
      • 8. Church, R.L., Stoms, D.M., Davis, F.W.: ‘Reserve selection as a maximal covering location problem’, Biol. Conserv., 1996, 76, (2), pp. 105112.
    21. 21)
      • 1. Hadley, M.: ‘A practical ecology the man and the biosphere (mab) programme’, Sixty Years of Science at UNESCO (2006) 260.
    22. 22)
      • 27. Royston, J.P.: ‘Algorithm as 181: the w test for normality’, J. R. Stat. Soc. Ser. C, Appl. Stat., 1982, 31, (2), pp. 176180.
    23. 23)
      • 4. I.U. for Conservation of Nature (IUCN), IUCN red list of threatened species, version 2011.1.
    24. 24)
      • 29. Royston, P.: ‘Remark as r94: a remark on algorithm as 181: the w-test for normality’, J. R. Stat. Soc. Ser. C, Appl. Stat., 1995, 44, (4), pp. 547551.
    25. 25)
      • 23. Wattenberg, M., Juels, A.: ‘Stochastic hill climbing as a baseline method for evaluating genetic algorithms’. Proc. of the 1995 Conf., 1996, vol. 8, p. 430.
    26. 26)
      • 10. Kincaid, R.K., Easterling, C., Jeske, M.: ‘Computational experiments with heuristics for two nature reserve site selection problems’, Comput. Oper. Res., 2008, 35, (2), pp. 499512.
    27. 27)
      • 19. Mirjalili, S., Lewis, A.: ‘S-shaped versus v-shaped transfer functions for binary particle swarm optimization’, Swarm Evol. Comput., 2013, 9, pp. 114.
    28. 28)
      • 16. Odili, J.B., Mohmad Kahar, M.N.: ‘Solving the traveling salesman's problem using the African buffalo optimization’, Comput. Intell. Neurosci., 2016, 2016, p. 3.
    29. 29)
      • 2. Ishwaran, N.: ‘Science in intergovernmental environmental relations: 40 years of UNESCO's man and the biosphere (MAB) programme and its future’, Environ. Dev., 2012, 1, (1), pp. 91101.
    30. 30)
      • 7. Underhill, L.: ‘Optimal and suboptimal reserve selection algorithms’, Biol. Conserv., 1994, 70, (1), pp. 8587.
    31. 31)
      • 12. Polasky, S., Camm, J.D., Garber-Yonts, B.: ‘Selecting biological reserves cost-effectively: an application to terrestrial vertebrate conservation in oregon’, Land Econom., 2001, 77, (1), pp. 6878.
    32. 32)
      • 3. Deguignet, M., Juffe-Bignoli, D., Harrison, J., et al: ‘United nations list of protected areas’ (UNEP-WCMC, Cambridge, UK), 44.
    33. 33)
      • 18. Almonacid, B.: ‘Simulation of a dynamic prey–predator spatial model based on cellular automata using the behavior of the metaheuristic African buffalo optimization’ (Springer International Publishing, Cham, 2017), pp. 170180.
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