Open-source code for the Spatial Optimization Model for Land Use (SSFLA-MLAS)

The open-source content is the source code of a land use spatial optimization model (SSFLA-MLAS) that couples the spatial leapfrog algorithm with multi-level multi-agent systems. The related paper is titled "Multi-objective synergy and hierarchical decision-making in land use optimization: Coupling bionic algorithm with multi-level agent". Since this research is still in the submission stage, the following is a summary of the research for ease of understanding.Land use optimization is a crucial method to reduce carbon emission and promote sustainable development. However, existing optimization models exhibit limitations in addressing complex human-land conflicts: (1) Local-scale models fail to incorporate spatial non-stationarity constraints and multi-level agent interactions, resulting in inadequate representation of heterogeneous decision-making. (2) Global-scale methods struggle with the endogenization of geographical rules, hindering effective translation of numerical solutions into spatial configurations. (3) The insufficient spatial-scale compatibility between global and local optimizations, coupled with shallow model integration and the absence of multi-scale feedback mechanisms, collectively constrain their application in supporting low-carbon-oriented territorial spatial management. Therefore, this study constructs a multi-objective optimization scenario under low-carbon development, introducing a new coupling model (SSFLA-MLAS). It designs a multi-level agent system (MLAS) where mapping and interaction coexist, and creates a spatialized frog leaping algorithm (SSFLA) by reconfiguring heuristic operators. The coupling of multi-models is achieved through an internal and external nested approach. The results, using Huangpi District as the experimental area, demonstrate that: (1) SSFLA-MLAS outperforms single algorithms in effectiveness and efficiency, achieving multi-objective benefits 3.4%, 32.5% and 26.5% greater than SSFLA, MLAS and MAS. (2) MLAS improves the comprehensive landscape pattern index by 26.4% over MAS while aligning spatial layouts with development strategies. (3) SSFLA-MLAS demonstrates remarkable optimization results, improving economic, social and ecological objectives by 5.01%, 2.87% and 0.74%, while enhancing carbon sinks by 1.38%. This research provides both a methodological framework for achieving sustainable development goals and novel insights into coupling global optimization with local simulation for modeling.