Genetic algorithms (GAs) have been widely applied in Steiner tree optimization problems. However, as the core operation, existing crossover operators for tree-based GAs suffer from producing illegal offspring trees. Therefore, some global link information must be adopted to ensure the connectivity of the offspring, which incurs heavy computation. To address this problem, this article proposes a new crossover mechanism, called leaf crossover (LC), which generates legal offspring by just exchanging partial parent chromosomes, requiring neither the global network link information, encoding/decoding nor repair operations. Our simulation study indicates that GAs with LC outperform GAs with existing crossover mechanisms in terms of not only producing better solutions but also converging faster in networks of varying sizes.

Genetic algorithms (GAs) serve as a class of powerful tools to search for an effective multicast routing scheme among multiple cluster header nodes, which strongly affects the lifetime of two-tiered Wireless sensor networks (WSNs). This paper proposes a novel Genetic algorithm (GA) with a new crossover mechanism called Leaf crossover for the multicast routing among upper tier nodes in two-tiered WSNs, which outperforms the existing popular tree-based GAs by not requiring the global network link information, encoding/decoding or repair operations. Our simulation study indicates that the proposed algorithm could prolong the lifetime of multicast service, increase the packet delivery ratio as well as converge fast by comparison with existing GAs.

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.