Strategic spatial planning is becoming more popular around the world as a decision-making way to build a unified vision for directing the medium- to long-term development of land/marine areas. Recently, the study of marine areas in terms of spatial planning such as Marine Spatial Planning (MSP) has received much attention. One of the challenging issues in MSP is to make a balance between determining the ideal zone for a new activity while also considering the locations of existing activities. This spatial zoning problem for multi-uses with multiple objectives could be formulated as optimization models. This paper presents and compares the results of two multi-objective evolutionary-based algorithms (MOEAs), Synchronous Hypervolume-based non-dominated sorting genetic algorithm-II (SH-NSGA-II) which is an extension of NSGA-II and a memetic algorithm (MA) in which SH-NSGA-II is enhanced with a local search. These proposed algorithms are used to solve the multi-objective spatial zoning optimization problem, which seeks to maximize the zone interest value assigned to the new activity while simultaneously maximizing its spatial compactness. We introduce several innovations in these proposed algorithms to address the problem constraints and to improve the robustness of the traditional NSGA-II and MA approaches. Unlike traditional ones, a different stop condition, multiple crossover, mutation, and repairing operators, and also a local search operator are developed. A comparative study is presented between the results obtained using both algorithms. To guarantee robust results for both algorithms, their parameters are calibrated and tuned using the Multi-Response Surface Methodology (MRSM) method. The effective and non-effective components, as well as the validity of the regression models, are determined using analysis of variance (ANOVA). Although SH-NSGA-II has revealed a good efficiency, its performance is still improved using a local search scheme within SH-NSGA-II, which is specially tailored to the problem characteristics. The two methods are designed for raster data.

战略空间规划作为构建统一愿景、指导陆地/海洋区域中长期发展的决策方式，在世界范围内越来越受欢迎。近年来，海洋空间规划（MSP）等海洋区域空间规划研究备受关注。MSP 的挑战性问题之一是在确定新活动的理想区域和考虑现有活动的地点之间取得平衡。这种具有多个目标的多种用途的空间分区问题可以表述为优化模型。本文介绍并比较了两种基于多目标进化的算法（MOEA）的结果，基于同步超体积的非支配排序遗传算法-II（SH-NSGA-II）是NSGA-II的扩展，也是一种模因算法（MA），其中SH-NSGA-II通过局部搜索得到增强。这些提出的算法用于解决多目标空间分区优化问题，该问题寻求最大化分配给新活动的区域兴趣值，同时最大化其空间紧凑性。我们在这些提出的算法中引入了多项创新，以解决问题约束并提高传统 NSGA-II 和 MA 方法的鲁棒性。与传统算子不同，开发了不同的停止条件、多个交叉、变异和修复算子，以及局部搜索算子。对使用两种算法获得的结果进行了比较研究。为了保证两种算法的稳健结果，它们的参数均使用多响应表面方法 (MRSM) 进行校准和调整。使用方差分析 (ANOVA) 确定有效和无效成分以及回归模型的有效性。尽管SH-NSGA-II显示出良好的效率，但使用SH-NSGA-II中专门针对问题特征定制的局部搜索方案，其性能仍然得到提高。这两种方法都是针对栅格数据而设计的。尽管SH-NSGA-II显示出良好的效率，但使用SH-NSGA-II中专门针对问题特征定制的局部搜索方案，其性能仍然得到提高。这两种方法都是针对栅格数据而设计的。尽管SH-NSGA-II显示出良好的效率，但使用SH-NSGA-II中专门针对问题特征定制的局部搜索方案，其性能仍然得到提高。这两种方法都是针对栅格数据而设计的。