@inproceedings{00065feeb97448dd8b710d80efc96c2f,
title = "Hierarchical heterogeneous particle swarm optimization",
abstract = "Particle swarm optimization (PSO) has recently been modified to several versions. Heterogeneous PSO is a recent extension which includes behavioral heterogeneity of particles. Here we propose a further developed version that has hierarchical interaction patterns among heterogeneous particles, which we call hierarchical heterogeneous PSO (HHPSO). Two algorithm designs that have been developed and tested are multi-layer HHPSO (ml-HHPSO) and multi-group HHPSO (mg-HHPSO). The performances of these algorithms were measured on a set of benchmark functions and compared with standard PSO and heterogeneous PSO. The results showed that the performances of both HHPSO algorithms were significantly improved from standard PSO and heterogeneous PSO, with higher quality of optimal solutions and faster convergence speed.",
author = "Xinpei Ma and Hiroki Sayama",
note = "Funding Information: This material is based upon work supported by the US National Science Foundation under Grant No. 1319152. Publisher Copyright: {\textcopyright} Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014. All rights reserved.; 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014 ; Conference date: 30-07-2014 Through 02-08-2014",
year = "2014",
language = "English",
series = "Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014",
publisher = "MIT Press Journals",
pages = "629--630",
editor = "Hiroki Sayama and John Rieffel and Sebastian Risi and Rene Doursat and Hod Lipson",
booktitle = "Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014",
address = "United States",
}