Yang Qiang, Chen Wei-Neng, Gu Tianlong, Zhang Huaxiang, Deng Jeremiah D, Li Yun, Zhang Jun
IEEE Trans Cybern. 2017 Sep;47(9):2896-2910. doi: 10.1109/TCYB.2016.2616170. Epub 2016 Oct 24.
Large-scale optimization has become a significant yet challenging area in evolutionary computation. To solve this problem, this paper proposes a novel segment-based predominant learning swarm optimizer (SPLSO) swarm optimizer through letting several predominant particles guide the learning of a particle. First, a segment-based learning strategy is proposed to randomly divide the whole dimensions into segments. During update, variables in different segments are evolved by learning from different exemplars while the ones in the same segment are evolved by the same exemplar. Second, to accelerate search speed and enhance search diversity, a predominant learning strategy is also proposed, which lets several predominant particles guide the update of a particle with each predominant particle responsible for one segment of dimensions. By combining these two learning strategies together, SPLSO evolves all dimensions simultaneously and possesses competitive exploration and exploitation abilities. Extensive experiments are conducted on two large-scale benchmark function sets to investigate the influence of each algorithmic component and comparisons with several state-of-the-art meta-heuristic algorithms dealing with large-scale problems demonstrate the competitive efficiency and effectiveness of the proposed optimizer. Further the scalability of the optimizer to solve problems with dimensionality up to 2000 is also verified.
大规模优化已成为进化计算中一个重要但具有挑战性的领域。为了解决这个问题，本文提出了一种新颖的基于片段的主导学习群体优化器（SPLSO），通过让几个主导粒子引导一个粒子的学习。首先，提出了一种基于片段的学习策略，将整个维度随机划分为片段。在更新过程中，不同片段中的变量通过从不同范例学习来进化，而同一片段中的变量则由同一个范例进化。其次，为了加速搜索速度并增强搜索多样性，还提出了一种主导学习策略，即让几个主导粒子引导一个粒子的更新，每个主导粒子负责一个维度片段。通过将这两种学习策略结合在一起，SPLSO同时进化所有维度，并具有竞争性的探索和利用能力。在两个大规模基准函数集上进行了广泛的实验，以研究每个算法组件的影响，并且与几种处理大规模问题的先进元启发式算法进行比较，证明了所提出优化器具有竞争效率和有效性。此外，还验证了该优化器在解决维度高达2000的问题时的可扩展性。
