Enhancing Learning Efficiency of Brain Storm Optimization via Orthogonal Learning Design

In brain storm optimization (BSO), the convergent operation utilizes a clustering strategy to group the population into multiple clusters, and the divergent operation uses this cluster information to generate new individuals. However, this mechanism is inefficient to regulate the exploration and exploitation search. This article first analyzes the main factors that influence the performance of BSO and then proposes an orthogonal learning framework to improve its learning mechanism. In this framework, two orthogonal design (OD) engines (i.e., exploration OD engine and exploitation OD engine) are introduced to discover and utilize useful search experiences for performance improvements. In addition, a pool of auxiliary transmission vectors with different features is maintained and their biases are also balanced by the OD decision mechanism. Finally, the proposed algorithm is verified on a set of benchmarks and is adopted to resolve the quantitative association rule mining problem considering the support, confidence, comprehensibility, and netconf. The experimental results show that the proposed approach is very powerful in optimizing complex functions. It not only outperforms previous versions of the BSO algorithm but also outperforms several famous OD-based algorithms.

在头脑风暴优化算法（BSO）中，收敛操作利用聚类策略将种群分组为多个簇，发散操作则使用该簇信息来生成新个体。然而，这种机制在调节探索和开发搜索方面效率低下。本文首先分析了影响BSO性能的主要因素，然后提出一个正交学习框架以改进其学习机制。在此框架中，引入两个正交设计（OD）引擎（即探索OD引擎和开发OD引擎）来发现和利用有用的搜索经验以提高性能。此外，维护一个具有不同特征的辅助传递向量池，并且它们的偏差也由OD决策机制进行平衡。最后，所提出的算法在一组基准测试上得到验证，并被用于解决考虑支持度、置信度、可理解性和净置信度的定量关联规则挖掘问题。实验结果表明，所提出的方法在优化复杂函数方面非常强大。它不仅优于BSO算法的先前版本，还优于几种著名的基于正交设计的算法。