Chaotic multi-group optimization algorithm based on heterogeneous computing

• Main Authors: Liu, Runjie, Cai, Genhao, Chen, Le, Wang, Siqi
• Format: Conference Proceeding
• Language: English
• Published: SPIE 07.12.2023
• ISBN: 9781510671881; 1510671889
• ISSN: 0277-786X
• DOI: 10.1117/12.3011967

Traditional particle swarm optimization has attracted attention in various fields because of its relatively simple form and flexible parameter setting, but it also has the disadvantages of slow convergence speed and easy to fall into local optimization in the face of large-scale multivariate data. To solve this kind of problem, a chaotic multi-group optimization algorithm (CM-PSO) based on Graphics Processing Unit (GPU) is proposed. In the algorithm initialization stage, chaotic mapping is introduced to enhance population diversity, and then the population is divided into multiple small subgroups according to the idea of island model, and the Feng's topology is adopted within each subgroup to improve the search efficiency and reduce the computational complexity. Finally, the CUDA stream (streams) technology is used to realize grid-level parallelism, further improve the degree of algorithm parallelism, and improve the algorithm performance while ensuring the accuracy of the algorithm.