Simulating complex urban growth using agent based modeling and genetic algorithms

Urban growth, particularly in the Global South, poses significant challenges due to the emergence of informal settlements as well as planned areas. Existing urban growth models, including multi-agent systems, often rely on rigid spatial structures and strong hypothesis, which are poorly suited for both contexts. This paper introduces a novel method that combines procedural generation and genetic algorithms to model spatial dynamics in agent-based urban growth simulations. By defining two types of spatial influence functions and optimizing their parameters using a genetic algorithm (NSGA-II), our approach eliminates the need for empirical assumptions and improves realism by learning from spatial data. Experiments demonstrate the applicability of this method in generating realistic informal settlements and planned areas, validated through a combination of density and distance measures. This study contributes to the field by providing a more flexible and accurate model for simulating complex urban growth. • Introduction of a generic agent-based model for complex urban growth. • Definition of spatial influence functions to model environmental factors. • Introduction of a novel method combining procedural generation and genetic algorithms for urban growth simulation. • Validation of the model through a combination of spatial measures estimating density and distance differences. • Demonstration of the method’s applicability in generating realistic informal settlements and planned areas.