Multi-objective land allocation (MOLA) can be regarded as a spatial optimization problem that allocates appropriate use to certain land units subjecting to multiple objectives and constraints. This article develops an improved knowledge-informed non-dominated sorting genetic algorithm II (NSGA-II) for solving the MOLA problem by integrating the patch-based, edge growing/decreasing, neighborhood, and constraint steering rules. By applying both the classical and the knowledge-informed NSGA-II to a simulated planning area of 30 × 30 grid, we find that: when compared to the classical NSGA-II, the knowledge-informed NSGA-II consistently produces solutions much closer to the true Pareto front within shorter computation time without sacrificing the solution diversity; the knowledge-informed NSGA-II is more effective and more efficient in encouraging compact land allocation; the solutions produced by the knowledge-informed have less scattered/isolated land units and provide a good compromise between construction sprawl and conservation land protection. The better performance proves that knowledge-informed NSGA-II is a more reasonable and desirable approach in the planning context.

Multi-objective land allocation (MOLA) can be regarded as a spatial optimization problem that allocates appropriate use to specific land units concerning some objectives and constraints. Simulating annealing (SA), genetic algorithm (GA), and particle swarm optimization (PSO) have been popularly applied to solve MOLA problems, but their performance has not been well evaluated. This paper applies the three algorithms to a common MOLA problem that aims to maximize land suitability and spatial compactness and minimize land conversion cost subject to the number of units allocated for each use. Their performance has been evaluated based on the solution quality and the computational cost. The results demonstrate that: (1) GA consistently achieves quality solutions that satisfy both the objectives and the constraints and the computational cost is lower. (2) The popular penalty function method does not work well for SA in handling the constraints. (3) The solution quality of PSO needs to be improved. Techniques that better adapt PSO for discrete variables in MOLA problems need to be developed. (4) All three algorithms take high computational costs to achieve quality solutions in handling the objective of maximizing spatial compactness. How to encourage compact allocation is a common problem for them.

“Three-space” (including agricultural space, urban and rural construction space, and ecological space) and “three-line” (including urban development boundary, prime farmland control line, basic ecological control line) planning has been regarded as an essential measure for China’s city and county level “multiple-plan integration”. It handles the multiple planning objectives of development management, agricultural land preservation, and ecological resource protection. This article proposes a rational planning with multi-criteria evaluation and spatial optimization (RP-MCE-SOP) framework for China’s county-level “three-space” and “three-line” planning by following the rational planning (RP) model and taking advantages of multi-criteria evaluation (MCE) and spatial optimization (SOP) techniques. The framework includes five steps of building the SOP model, land suitability evaluation with MCE, optimization problem solving, post-processing of land allocation solutions, and applying post-processed solutions to “three-space” and “three-line” planning. The framework was implemented in Dongxihu District of Wuhan City with the Boolean aggregation and analytical hierarchy analysis (AHP) MCE techniques and the patch-based Non-dominated Genetic Algorithm (NSGA-II) SOP algorithm. The case study shows: (1) The framework is feasible and useful for assisting decision making in “three-space” and “three-line” planning. (2) The planning solutions protect ecologically sensitive spaces and high-quality agricultural land and plan future construction in the urban peripheral area or transportation convenient areas. (3) The solutions are useful for planning the hard boundaries for ecological resource protection and prime farmland preservation and setting both hard and soft boundaries for urban growth.