IEEE Trans Cybern. 2022 Jul;52(7):6707-6720. doi: 10.1109/TCYB.2020.3032995. Epub 2022 Jul 4.
Multimodal optimization problems (MMOPs) are common problems with multiple optimal solutions. In this article, a novel method of population division, called nearest-better-neighbor clustering (NBNC), is proposed, which can reduce the risk of more than one species locating the same peak. The key idea of NBNC is to construct the raw species by linking each individual to the better individual within the neighborhood, and the final species of the population is formulated by merging the dominated raw species. Furthermore, a novel algorithm is proposed called NBNC-PSO-ES, which combines the advantages of better exploration in particle swarm optimization (PSO) and stronger exploitation in the covariance matrix adaption evolution strategy (CMA-ES). For the purpose of demonstrating the performance of NBNC-PSO-ES, several state-of-the-art algorithms are adopted for comparisons and tested using typical benchmark problems. The experimental results show that NBNC-PSO-ES performs better than other algorithms.
多模态优化问题（MMOPs）是具有多个最优解的常见问题。本文提出了一种新的种群划分方法，称为最近较好邻居聚类（NBNC），可以降低多个物种定位在同一峰值的风险。NBNC 的关键思想是通过将每个个体与邻域内更好的个体连接来构建原始个体，然后通过合并占主导地位的原始个体来形成种群的最终个体。此外，还提出了一种新的算法，称为 NBNC-PSO-ES，它结合了粒子群优化（PSO）在探索方面的优势和协方差矩阵自适应进化策略（CMA-ES）在开发方面的优势。为了展示 NBNC-PSO-ES 的性能，采用了几种最先进的算法进行比较，并使用典型的基准问题进行了测试。实验结果表明，NBNC-PSO-ES 优于其他算法。
