A self-adaptive differential evolution algorithm with robust decision-making for school layout problem

As urban populations expand and student distributions become increasingly uncertain, the optimisation of school layout problem (SLP) faces growing challenges in terms of solution robustness. However, robust decision-making has received limited attention in SLP literature. This study proposes a self-adaptive differential evolution algorithm with robust decision-making (SaDE-RD) to address SLP. A robustness evaluation model is established using two budget uncertainty sets that capture fluctuations in student numbers and commuting distances and reformulated into a tractable linear form via robust optimisation theory. To enhance search efficiency, SaDE-RD introduces a self-adaptive parameter control mechanism that dynamically adjusts the scaling factor and crossover rate on the basis of the rate of best-solution updates. Moreover, a new stagnation process strategy is employed to maintain high diversity by retaining suboptimal solutions. Extensive experiments on real-world and benchmark datasets demonstrate that SaDE-RD outperforms baseline algorithms in overall quality.