Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO

• URL: http://arxiv.org/abs/2108.11173v3
• Date: Wed, 13 Sep 2023 00:57:01 GMT
• Title: Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO
• Authors: Xuan Wu, Jizong Han, Di Wang, Pengyue Gao, Quanlong Cui, Liang Chen, Yanchun Liang, Han Huang, Heow Pueh Lee, Chunyan Miao, You Zhou, and Chunguo Wu
• Abstract summary: We adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. We propose a Euclidean distance-based adaptive topology to cooperate with SPA. We show that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems.
• Score: 42.6811816733091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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