Bio-inspired optimization aims at adapting observed natural behavioral patterns and social phenomena towards efficiently solving complex optimization problems, and is nowadays gaining much attention. However, researchers recently highlighted an inconsistency between the need in the field and the actual trend. Indeed, while nowadays it is important to design innovative contributions, an actual trend in bio-inspired optimization is to re-iterate the existing knowledge in a different form. The aim of this paper is to fill this gap. More precisely, we start first by highlighting new examples for this problem by considering and describing the concepts of chunking and cooperative learning. Second, by considering particle swarm optimization (PSO), we present a novel bridge between these two notions adapted to the problem of feature selection. In the experiments, we investigate the practical importance of our approach while exploring both its strength and limitations. The results indicate that the approach is mainly suitable for large datasets, and that further research is needed to improve the computational efficiency of the approach and to ensure the independence of the sub-problems defined using chunking.

仿生优化旨在适应观察到的自然行为模式和社会现象，以有效解决复杂的优化问题，目前正受到广泛关注。然而，研究人员最近强调了该领域的需求与实际趋势之间的不一致。事实上，虽然现在设计创新贡献很重要，但仿生优化的实际趋势是以不同的形式重复现有知识。本文旨在填补这一空白。更准确地说，我们首先通过考虑和描述组块和合作学习的概念来强调这个问题的新例子。其次，通过考虑粒子群优化 (PSO)，我们在这两个概念之间架起了一座适用于特征选择问题的新桥梁。在实验中，我们调查了我们的方法的实际重要性，同时探索了它的优势和局限性。结果表明该方法主要适用于大型数据集，需要进一步研究以提高该方法的计算效率并确保使用分块定义的子问题的独立性。