Incorporating Surprisingly Popular Algorithm and Euclidean distance-based adaptive topology into PSO

作者：Wu, Xuan; Han, Jizong; Wang, Di; Gao, Pengyue; Cui, Quanlong; Chen, Liang; Liang, Yanchun; Huang, Han; Lee, Heow Pueh; Miao, Chunyan; Zhou, You^*; Wu, Chunguo^*

来源：Swarm and Evolutionary Computation, 2023, 76: 101222.

DOI：10.1016/j.swevo.2022.101222

• Particle swarm optimization
• Surprisingly popular algorithm
• Small-world network
• Heterogeneous PSO
• CEC benchmark suites

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.

• 单位
  汕头大学; 南阳理工学院; y; 吉林大学