Work

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

Year: 2026

Type: article

Abstract: 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.

Source: PeerJ Computer Science

DOI: 10.7717/peerj-cs.3738

Authors: Qiongbing Zhang, Yating Lin, Liang Ouyang, Jiaxin Fang

Institution: Hunan University of Science and Technology

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,395

Funder: National Natural Science Foundation of China