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

TL;DR

This paper enhances Particle Swarm Optimization by integrating the Surprisingly Popular Algorithm for better particle selection and introducing a Euclidean distance-based adaptive topology, improving performance across various problem scales.

Contribution

It introduces a novel combination of SPA and adaptive topology into PSO, especially for large-scale problems, improving diversity and convergence.

Findings

Outperforms traditional topologies in experiments

Significantly better results than state-of-the-art PSO variants

Effective on small, medium, and large-scale problems

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.