Incorporating historical information into the multi-type ant colony optimization model to optimize patch-level land use allocation

Abstract

Land use optimization is a crucial approach for promoting sustainable development. However, current land use optimization models usually ignore the temporal pattern of land use evolution. Research on how to quantify and incorporate historical information into land use optimization process is scarce, and worth exploring. This study proposed a historical-information-based patch-level multi-type ant colony optimization (HI-PMACO) model to examine the effect of incorporating historical information into the optimization model from both macro and local perspectives. Specifically, we (1) constructed five machine learning models to explore the transition potentials and used the SHAP algorithm to visualize the non-linear effects of spatial variables; (2) introduced a size-adaptive neighborhood strategy to quantify the local land use evolution preference; (3) designed an evolution preference-weighted roulette wheel mechanism to incorporate the extracted historical information into the biomimetic intelligent algorithm; and (4) verified the effect of the HI-PMACO model. Results demonstrated that the proposed HI-PMACO model exhibited better performance in improving economic benefit and transition potential objectives with smaller land use change costs, and the local details were highly in line with relevant planning policies. This study contributes to spatio-temporal land use optimization modeling in methodology and land use management considering temporal evolution patterns.