While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems.

翻译：尽管许多粒子群优化算法仅使用适应度来评估粒子的性能，但本研究引入惊异流行算法作为适应度的补充指标。这使得未被广泛认知的粒子也有机会被选为学习榜样。此外，我们提出了一种基于欧氏距离的自适应拓扑结构与惊异流行算法协同工作——在每次迭代中，每个粒子仅与欧氏距离最短的k个粒子建立连接。我们还将自适应拓扑引入异质种群以更好地解决大规模问题：探索子种群能更好地保持种群多样性，而开发子种群则实现快速收敛。由此，大规模问题可通过协同方式求解以提升整体性能。为评估方法有效性，我们在多种优化问题上进行了广泛实验，包括三个基准测试套件和两个实际优化问题。结果表明，我们提出的基于欧氏距离的自适应拓扑优于其他广泛采用的拓扑结构，且该方法在中小规模及大规模问题上的性能显著优于当前最先进的粒子群优化变体。