Abstract
In this paper, we analyze in detail the properties of the newly developed Adaptive Wind Driven Optimization (AWDO) method and demonstrate its efficiency on continuous-valued electromagnetic (EM) problems by comparing it to the performance of the classical Wind Driven Optimization (WDO) method. While WDO has proven to be competitive with similar algorithms such as Particle Swarm Optimization (PSO), there are a large number of inherent parameters in the WDO update equations which the user needs to fine tune for optimal performance. The newly developed AWDO method automatically determines these parameters, creating an adaptive algorithm which eliminates the need for the user to have apriori knowledge of the optimal values for the inherent parameters. Numerical optimization results on EM problems indicate that AWDO matches or surpasses the performance of WDO, yielding a self-adaptive algorithm which is both extremely efficient as well as easy to use.
| Original language | English (US) |
|---|---|
| Title of host publication | 2018 International Applied Computational Electromagnetics Society Symposium in Denver, ACES-Denver 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9780996007870 |
| DOIs | |
| State | Published - May 23 2018 |
| Event | 2018 International Applied Computational Electromagnetics Society Symposium in Denver, ACES-Denver 2018 - Denver, United States Duration: Mar 25 2018 → Mar 29 2018 |
Other
| Other | 2018 International Applied Computational Electromagnetics Society Symposium in Denver, ACES-Denver 2018 |
|---|---|
| Country | United States |
| City | Denver |
| Period | 3/25/18 → 3/29/18 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Computational Mathematics
- Numerical Analysis
- Instrumentation
Cite this
}
The adaptive wind driven optimization and its application in electromagnetics. / Nagar, Jogender; Campbell, Sawyer; Werner, Douglas Henry; Bayraktar, Zikri; Komurcu, Muge.
2018 International Applied Computational Electromagnetics Society Symposium in Denver, ACES-Denver 2018. Institute of Electrical and Electronics Engineers Inc., 2018.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - The adaptive wind driven optimization and its application in electromagnetics
AU - Nagar, Jogender
AU - Campbell, Sawyer
AU - Werner, Douglas Henry
AU - Bayraktar, Zikri
AU - Komurcu, Muge
PY - 2018/5/23
Y1 - 2018/5/23
N2 - In this paper, we analyze in detail the properties of the newly developed Adaptive Wind Driven Optimization (AWDO) method and demonstrate its efficiency on continuous-valued electromagnetic (EM) problems by comparing it to the performance of the classical Wind Driven Optimization (WDO) method. While WDO has proven to be competitive with similar algorithms such as Particle Swarm Optimization (PSO), there are a large number of inherent parameters in the WDO update equations which the user needs to fine tune for optimal performance. The newly developed AWDO method automatically determines these parameters, creating an adaptive algorithm which eliminates the need for the user to have apriori knowledge of the optimal values for the inherent parameters. Numerical optimization results on EM problems indicate that AWDO matches or surpasses the performance of WDO, yielding a self-adaptive algorithm which is both extremely efficient as well as easy to use.
AB - In this paper, we analyze in detail the properties of the newly developed Adaptive Wind Driven Optimization (AWDO) method and demonstrate its efficiency on continuous-valued electromagnetic (EM) problems by comparing it to the performance of the classical Wind Driven Optimization (WDO) method. While WDO has proven to be competitive with similar algorithms such as Particle Swarm Optimization (PSO), there are a large number of inherent parameters in the WDO update equations which the user needs to fine tune for optimal performance. The newly developed AWDO method automatically determines these parameters, creating an adaptive algorithm which eliminates the need for the user to have apriori knowledge of the optimal values for the inherent parameters. Numerical optimization results on EM problems indicate that AWDO matches or surpasses the performance of WDO, yielding a self-adaptive algorithm which is both extremely efficient as well as easy to use.
UR - http://www.scopus.com/inward/record.url?scp=85048332713&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048332713&partnerID=8YFLogxK
U2 - 10.23919/ROPACES.2018.8364096
DO - 10.23919/ROPACES.2018.8364096
M3 - Conference contribution
AN - SCOPUS:85048332713
BT - 2018 International Applied Computational Electromagnetics Society Symposium in Denver, ACES-Denver 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -