Abstract
Among decision problems in spatial management planning, marine spatial planning (MSP) has lately gained popularity. One of the difficulties in MSP is to determine the best place for a new activity while taking into account the locations of current activities. This paper presents the results of the extension of one multi-objective evolutionary-based algorithm (MOEA), non-dominated sorting genetic algorithm-II (NSGA-II) solved the multi-objective spatial zoning optimization problem. The proposed algorithm aims to maximize the interest of the area of the zone dedicated to the new activity while maximizing its spatial compactness. The extended NSGA-II, unlike the traditional one, makes use of a different stop condition, four crossover operators, three mutation operators, and repairing operators. This algorithm is developed for the raster data and it computes solutions for the multi-objective spatial zoning optimization model at a large scale. The proposed NSGA-II has revealed a good performance in comparison with the exact method tested on a small scale. To improve the performance of the algorithm, its parameters are calibrated and tuned using the Multi-Response Surface Methodology (MRSM) method. Analysis of variance (ANOVA) was used to determine the effective and non-effective factors and correctness of the regression models. Finally, conclusions are made and future research works are recommended.
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References
Basirati, M., Akbari Jokar, M.R., Hassannayebi, E.: Bi-objective optimization approaches to many-to-many hub location routing with distance balancing and hard time window. Neural Comput. Appl. 32(17), 13267–13288 (2020)
Basirati, M., Billot, R., Meyer, P., Bocher, E.: Exact zoning optimization model for marine spatial planning (MSP). Front. Marine Sci. 8, 726187 (2021)
Censor, Y.: Pareto optimality in multiobjective problems. Appl. Math. Optim. 4(1), 41–59 (1977)
Deb, K.: Multi-objective optimisation using evolutionary algorithms: an introduction. In: Wang, L., Ng, A., Deb, K. (eds.) Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. Springer, London (2011). https://doi.org/10.1007/978-0-85729-652-8_1
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., et al. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45356-3_83
Gwaleba, M.J., Chigbu, U.E.: Participation in property formation: Insights from land-use planning in an informal urban settlement in tanzania. Land Use Policy 92, 104482 (2020)
Heckert, N.A., et al.: Handbook 151: Nist/sematech e-handbook of statistical methods. In: e-Handbook of Statistical Methods, pp. 2 (2002)
Hejazi, T.H., Bashiri, M., Dı, J.A., Noghondarian, K., et al.: Optimization of probabilistic multiple response surfaces. Appl. Math. Model. 36(3), 1275–1285 (2012)
Lokman, B., Köksalan, M., Korhonen, P.J., Wallenius, J.: An interactive approximation algorithm for multi-objective integer programs. Comput. Oper. Res. 96, 80–90 (2018)
Myers, R.H., Montgomery, D.C., Vining, G.G., Borror, C.M., Kowalski, S.M.: Response surface methodology: a retrospective and literature survey. J. Qual. Technol. 36(1), 53–77 (2004)
Paquete, L., Schulze, B., Stiglmayr, M., Lourenço, A.C.: Computing representations using hypervolume scalarizations. Comput. Oper. Res. 137, 105349 (2022)
Sidi, M.O., Kadrani, A., Quilot-Turion, B., Lescourret, F., Génard, M.: Compromising NSGA-II performances and stopping criteria: case of virtual peach design. In: International Conference on Metamaterials, Photonic Crystals and Plasmonics, p. 2 (2012)
Stewart, T.J., Janssen, R., van Herwijnen, M.: A genetic algorithm approach to multiobjective land use planning. Comput. Oper. Res. 31(14), 2293–2313 (2004)
Talbi, E.G.: Metaheuristics: from design to implementation, vol. 74. John Wiley & Sons (2009)
Wenwen, L., Goodchild, F., Church, R.: An efficient measure of compactness for 2d shapes and its application in regionalization problems. Int. J. Geograph. Info Sci. 27(6), 1227–1250 (2013)
Zanakis, S.H., Solomon, A., Wishart, N., Dublish, S.: Multi-attribute decision making: A simulation comparison of select methods. Eur. J. Oper. Res. 107(3), 507–529 (1998)
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Basirati, M., Billot, R., Meyer, P. (2022). An Extension of NSGA-II for Scaling up Multi-objective Spatial Zoning Optimization. In: Simos, D.E., Rasskazova, V.A., Archetti, F., Kotsireas, I.S., Pardalos, P.M. (eds) Learning and Intelligent Optimization. LION 2022. Lecture Notes in Computer Science, vol 13621. Springer, Cham. https://doi.org/10.1007/978-3-031-24866-5_16
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