Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO
This work addresses optimization challenges in PSO for researchers and practitioners, but it is incremental as it builds on existing PSO methods with new metrics and topologies.
The authors tackled the problem of improving Particle Swarm Optimization (PSO) by incorporating the Surprisingly Popular Algorithm (SPA) as a complementary metric to fitness and proposing a Euclidean distance-based adaptive topology, resulting in their method outperforming state-of-the-art PSO variants on small, medium, and large-scale optimization problems.
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.