Land-use planning via enhanced multi-objective evolutionary algorithms: optimizing the land value of major Greenfield initiatives

The successful implementation of major development initiatives relies on the sound allocation of land uses against critical design criteria and constraints. The discovery of optimum development plans introduces severe complexities in formulating and solving the underlying multi-objective optimization problem. Moreover, in the presence of conflicting planning criteria decision-makers should be provided with a set of alterative-yet-optimum solutions that uniformly cover the spectrum of feasible maps. The introduction of sophisticated optimization algorithms addresses this challenge by pursuing a complete approximation of the Pareto front containing all prominent spatial allocations. This study demonstrates the effectiveness of a new evolutionary algorithm (UDT-MOEA) against the results of an established multi-objective genetic algorithm (NSGA-II) when applied on a major greenfield initiative against the optimum location(s), size and shape of three new land uses. Each algorithm performs best in different areas of the feasible objective space, providing planning alternatives with distinct characteristics.