IEICE TRANSACTIONS on Fundamentals
Consensus-Based Distributed Particle Swarm Optimization with Event-Triggered Communication
Summary :
This paper proposes a consensus-based distributed Particle Swarm Optimization (PSO) algorithm with event-triggered communications for a non-convex and non-differentiable optimization problem. We consider a multi-agent system whose local communications among agents are represented by a fixed and connected graph. Each agent has multiple particles as estimated solutions of global optima and updates positions of particles by an average consensus dynamics on an auxiliary variable that accumulates the past information of the own objective function. In contrast to the existing time-triggered approach, the local communications are carried out only when the difference between the current auxiliary variable and the variable at the last communication exceeds a threshold. We show that the global best can be estimated in a distributed way by the proposed event-triggered PSO algorithm under a diminishing condition of the threshold for the trigger condition.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E101-A No.2 pp.338-344
- Publication Date
- 2018/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E101.A.338
- Type of Manuscript
- Special Section PAPER (Special Section on Mathematical Systems Science and its Applications)
- Category
Authors
Kazuyuki ISHIKAWA
Osaka University
Naoki HAYASHI
Osaka University
Shigemasa TAKAI
Osaka University
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Kazuyuki ISHIKAWA, Naoki HAYASHI, Shigemasa TAKAI, "Consensus-Based Distributed Particle Swarm Optimization with Event-Triggered Communication" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 2, pp. 338-344, February 2018, doi: 10.1587/transfun.E101.A.338.
Abstract: This paper proposes a consensus-based distributed Particle Swarm Optimization (PSO) algorithm with event-triggered communications for a non-convex and non-differentiable optimization problem. We consider a multi-agent system whose local communications among agents are represented by a fixed and connected graph. Each agent has multiple particles as estimated solutions of global optima and updates positions of particles by an average consensus dynamics on an auxiliary variable that accumulates the past information of the own objective function. In contrast to the existing time-triggered approach, the local communications are carried out only when the difference between the current auxiliary variable and the variable at the last communication exceeds a threshold. We show that the global best can be estimated in a distributed way by the proposed event-triggered PSO algorithm under a diminishing condition of the threshold for the trigger condition.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.338/_p
Copy
@ARTICLE{e101-a_2_338,
author={Kazuyuki ISHIKAWA, Naoki HAYASHI, Shigemasa TAKAI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Consensus-Based Distributed Particle Swarm Optimization with Event-Triggered Communication},
year={2018},
volume={E101-A},
number={2},
pages={338-344},
abstract={This paper proposes a consensus-based distributed Particle Swarm Optimization (PSO) algorithm with event-triggered communications for a non-convex and non-differentiable optimization problem. We consider a multi-agent system whose local communications among agents are represented by a fixed and connected graph. Each agent has multiple particles as estimated solutions of global optima and updates positions of particles by an average consensus dynamics on an auxiliary variable that accumulates the past information of the own objective function. In contrast to the existing time-triggered approach, the local communications are carried out only when the difference between the current auxiliary variable and the variable at the last communication exceeds a threshold. We show that the global best can be estimated in a distributed way by the proposed event-triggered PSO algorithm under a diminishing condition of the threshold for the trigger condition.},
keywords={},
doi={10.1587/transfun.E101.A.338},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - Consensus-Based Distributed Particle Swarm Optimization with Event-Triggered Communication
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 338
EP - 344
AU - Kazuyuki ISHIKAWA
AU - Naoki HAYASHI
AU - Shigemasa TAKAI
PY - 2018
DO - 10.1587/transfun.E101.A.338
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2018
AB - This paper proposes a consensus-based distributed Particle Swarm Optimization (PSO) algorithm with event-triggered communications for a non-convex and non-differentiable optimization problem. We consider a multi-agent system whose local communications among agents are represented by a fixed and connected graph. Each agent has multiple particles as estimated solutions of global optima and updates positions of particles by an average consensus dynamics on an auxiliary variable that accumulates the past information of the own objective function. In contrast to the existing time-triggered approach, the local communications are carried out only when the difference between the current auxiliary variable and the variable at the last communication exceeds a threshold. We show that the global best can be estimated in a distributed way by the proposed event-triggered PSO algorithm under a diminishing condition of the threshold for the trigger condition.
ER -
English
