IEEE Trans Cybern. 2021 Mar;51(3):1390-1402. doi: 10.1109/TCYB.2020.2967553. Epub 2021 Feb 17.
This article presents a surrogate-assisted multiswarm optimization (SAMSO) algorithm for high-dimensional computationally expensive problems. The proposed algorithm includes two swarms: the first one uses the learner phase of teaching-learning-based optimization (TLBO) to enhance exploration and the second one uses the particle swarm optimization (PSO) for faster convergence. These two swarms can learn from each other. A dynamic swarm size adjustment scheme is proposed to control the evolutionary progress. Two coordinate systems are used to generate promising positions for the PSO in order to further enhance its search efficiency on different function landscapes. Moreover, a novel prescreening criterion is proposed to select promising individuals for exact function evaluations. Several commonly used benchmark functions with their dimensions varying from 30 to 200 are adopted to evaluate the proposed algorithm. The experimental results demonstrate the superiority of the proposed algorithm over three state-of-the-art algorithms.
本文提出了一种基于代理的多群优化（SAMSO）算法，用于解决高维计算密集型问题。该算法包括两个群体：第一个群体使用基于教与学的优化（TLBO）的学习阶段来增强探索能力，第二个群体使用粒子群优化（PSO）来实现更快的收敛。这两个群体可以相互学习。提出了一种动态群体大小调整方案来控制进化过程。使用两个坐标系来为 PSO 生成有前途的位置，以进一步提高其在不同函数景观上的搜索效率。此外，还提出了一种新的预筛选标准来选择有前途的个体进行精确函数评估。采用了几个常用的基准函数，其维度从 30 到 200 不等，以评估所提出的算法。实验结果表明，该算法优于三种最先进的算法。
