A Heuristic Group Intelligence Control System Based on Hybrid Particle Swarm Gravitational Search Algorithm

DOI

10.2991/ammsa-17.2017.95How to use a DOI?

Keywords

cluster control; particle swarm optimization; gravitational search algorithm; distributed control algorithm; hybrid particle swarm gravitational search algorithm; tool-path optimization

Abstract

Cluster control system to realize mutual coordination between individual objects must determine the control and the information relationship in terms of logical and physical aspects. Study these problems of the system structure, system structure and control can be combined and ensure smooth information flow and control flow in the system and the framework for the interaction between the individual. Cluster control algorithm ensure cooperation among multiple control individual effectively, to deal with an emergency to be able to react quickly, improve the real-time performance and effectiveness of control system. This paper proposed a new methodology to Group Intelligence Control by using a distributed control algorithm and a Swarm Intelligence algorithm to optimize the path of the individual of the cluster and the real-time control of each object. In the first part of this paper, we introduced the appearance and the development of Group Intelligence Control system. Then, the paper proposed our methodology of the proposed Group Intelligence Control system.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Linshan Ding
AU  - Zhengya Wang
AU  - Tao Hu
AU  - Yi Liu
AU  - Li Zhang
PY  - 2017/05
DA  - 2017/05
TI  - A Heuristic Group Intelligence Control System Based on Hybrid Particle Swarm Gravitational Search Algorithm
BT  - Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)
PB  - Atlantis Press
SP  - 423
EP  - 426
SN  - 1951-6851
UR  - https://doi.org/10.2991/ammsa-17.2017.95
DO  - 10.2991/ammsa-17.2017.95
ID  - Ding2017/05
ER  -