An improved non-dominated sorting biogeography-based optimization algorithm for multi-objective land-use allocation: a case study in Kigali-Rwanda

With the continuous increase of rapid urbanization and population growth, sustainable urban land-use planning is becoming a more complex and challenging task for urban planners and decision-makers. Multi-objective land-use allocation can be regarded as a complex spatial optimization problem that aims to achieve the possible trade-offs among multiple and conflicting objectives. This paper proposes an improved Non-dominated Sorting Biogeography-Based Optimization (NSBBO) algorithm for solving the multi-objective land-use allocation problem, in which maximum accessibility, maximum compactness, and maximum spatial integration were formulated as spatial objectives; and space syntax analysis was used to analyze the potential movement patterns in the new urban planning area of the city of Kigali, Rwanda. Efficient Non-dominated Sorting (ENS) algorithm and crossover operator were integrated into classical NSBBO to improve the quality of non-dominated solutions, and local search ability, and to accelerate the convergence speed of the algorithm. The results showed that the proposed NSBBO exhibited good optimal solutions with a high hypervolume index compared to the classical NSBBO. Furthermore, the proposed algorithm could generate optimal land use scenarios according to the preferred objectives, thus having the potential to support the decision-making of urban planners and stockholders in revising and updating the existing detailed master plan of land use.

Automated summary

This paper proposes an improved NSBBO algorithm for solving the multi-objective land-use allocation problem, and applies it to spatial analysis of the new urban planning area in Kigali, Rwanda. The results show that the algorithm can generate optimal land-use scenarios and support decision-making by urban planners and stakeholders.