Integrating Planning Knowledge into Evolutionary Search: LLM-Based Qualitative Objectives for Urban Development Optimization

Multi-objective urban development optimization predominantly relies on quantitative indicators, limiting the inclusion of weakly formalized urban qualities. This study aims to incorporate qualitative planning priorities directly into scenario generation. The proposed approach uses a large language model to score criteria expressed in natural language, integrating these scores as an objective in evolutionary optimization alongside investor net present value and predicted land-value gain. Its methodological novelty lies in embedding language-based evaluation within solution generation and selection. Its conceptual contribution is to extend objective formulation in multi-objective search to weakly formalized planning priorities. The approach was tested on development scenarios for Gatchina, Russia, using five qualitative criteria. Intraclass correlations for means of five repeated evaluations reached 0.92–0.93. After three independent evaluations per solution for both methods, direct integration achieved a larger three-dimensional hypervolume in 27 of 50 matched-seed comparisons, with positive median differences for two of five criteria. Fronts pooled across seeds achieved larger three-dimensional hypervolume for all five criteria, indicating broader aggregate coverage despite variable performance in individual runs. These findings demonstrate the potential to expand planning alternatives and reveal additional trade-offs between economic and qualitative objectives. Practically, the approach enables qualitative urban priorities to guide automated scenario search and supports assessment of the economic trade-offs associated with pursuing them.