A comparative study of GPU metaheuristics for data clustering
Mario Santos, Bruno Costa e Silva Nogueira, Rian G. S. Pinheiro, Almir Pereira Guimarães, Albanita Ferreira Lima, Ermeson Andrade · 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2021
In this work, we conduct a comparative study of GPU accelerated metaheuristics for data clustering. Three population-based metaheuristics were implemented in GPU: Particle Swarm Optimization (PSO), Differential Evolution (DE), Scatter Search (SS). These metaheuristics were compared with the state-of-the-art methods for data clustering considering both runtime efficiency and solution quality. GPU-PSO and GPU-DE algorithms demonstrated competitive performance in the data sets proposed by the literature, as well as real-world problems. Moreover, experimental results show that our GPU proposal obtained an average speedup of 175x over the CPU-only implementation.