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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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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