Shuffled Frog leaping algorithm (SFLA) is a multi population swarm intelligence algorithm which employs population partitioning techniques during the evolutionary stage. Methods adopted by SFLA for partitioning the population into memeplexes play a critical role in determining its ability to solve complex optimization problems. However, limited research is done in this direction. This work presents supervised machine learning based methods Spectral Partitioning (SCP), Agglomerative Partitioning (AGP) and Ward Hierarchical Partitioning (WHP) for distributing the solutions into memeplexes. The efficacy of variants of SFLA with these methods is assessed over CEC2015 Bound Constrained Single-Objective Computationally Expensive Numerical Optimisation problems. Analysis of results establishes that proposed SCP, AGP and WHP methods outperform Shuffled complex evolution (SCE) partitioning technique; Seed and distance based partitioning technique (SEED), Random partitioning (RAND) and Dynamic sub-swarm partitioning (DNS) for more than 10 functions. Time complexity of all the algorithms is comparable with each other. Statistical analysis using Wilcoxon signed rank sum test indicates that SCP, AGP and WHP perform significantly better than existing approaches for small dimensions.

洗牌青蛙跳跃算法（SFLA）是一种多种群群体智能算法，在进化阶段采用种群划分技术。 SFLA 将群体划分为 memeplex 的方法在确定其解决复杂优化问题的能力方面发挥着关键作用。然而，这方面的研究有限。这项工作提出了基于监督机器学习的方法频谱分区（SCP）、聚合分区（AGP）和沃德分层分区（WHP），用于将解决方案分配到模因丛中。使用这些方法的 SFLA 变体的功效通过 CEC2015 约束单目标计算昂贵的数值优化问题进行评估。结果分析表明，所提出的 SCP、AGP 和 WHP 方法优于混洗复杂进化 (SCE) 划分技术；基于种子和距离的分区技术 (SEED)、随机分区 (RAND) 和动态子群分区 (DNS) 超过 10 种功能。所有算法的时间复杂度都具有可比性。使用 Wilcoxon 符号秩和检验的统计分析表明，对于小维度，SCP、AGP 和 WHP 的性能明显优于现有方法。