Multi-Objective Optimization of Residential Inducement Parameters in a Metropolitan Area Using a Genetic Algorithm

This study conducted multi-objective optimization using a genetic algorithm (GA) to identify optimal residential inducement parameters for future population distribution in the Nagoya metropolitan area, Japan. The design variables consisted of three parameters defining spatial zoning patterns and five parameters representing population change rates across different zones. Three objective functions were formulated to evaluate residential–railway station distance, flood exposure, and the scale of population redistribution across the entire area. By visualizing representative Pareto solutions on maps, this study demonstrates the potential applicability of GAs for examining population inducement measures.