Affiliated with the
Communication & Space
Sciences Laboratory

Nature Inspired Antenna Design

Wind Driven Optimization

The Window Driven Optimization (WDO) Algorithm is a stochastic population-based global optimizer inspired by atmospheric motion. The Wind Driven Optimization (WDO) technique is a population based iterative heuristic global optimization algorithm for multi-dimensional and multi-modal problems with the ability to implement constraints on the search domain. At its core, a population of infinitesimally small air parcels navigates over an N-dimensional search space following Newton's second law of motion, which is also used to describe the motion of air parcels within the earth's atmosphere. Compared to similar particle based algorithms, WDO employs additional terms in the velocity update equation (e.g. gravitation and Coriolis forces), providing robustness and extra degrees of freedom to fine tune the optimization. In December 2015, a new self-adaptive version of the WDO called the Adaptive Wind Driven Optimization (AWDO) was developed. CMA-ES is used in an outer iteration to tune the internal parameters of WDO, freeing the user from having to guess what the optimal parameters should be.

The low-pressure system depicted here spins counterclockwise due to a balance between the Coriolis force and the pressure gradient force.
Credit: By NASA’s Aqua/MODIS satellite

Potential trajectories of population members based on the Coriolis force (red) and the pressure gradient force (blue).
Credit: Roland Geider

Double-sided AMC optimized by WDO.

E-patch optimized using AWDO and simulated in FEKO. Results show a dual-band response.

..: Key Publication:..

  Z. Bayraktar, M. Komurcu, J. A. Bossard and D. H. Werner, "The Wind Driven Optimization Technique and its Application in Electromagnetics," IEEE Transactions on Antennas and Propagation, Vol. 61, No. 5, pages 2745 - 2757, May 2013.

ABSTRACT: A new type of nature-inspired global optimization methodology based on atmospheric motion is introduced. The proposed Wind Driven Optimization (WDO) technique is a population based iterative heuristic global optimization algorithm for multi-dimensional and multi-modal problems with the potential to implement constraints on the search domain. At its core, a population of infinitesimally small air parcels navigates over an N-dimensional search space following Newton's second law of motion, which is also used to describe the motion of air parcels within the earth's atmosphere. Compared to similar particle based algorithms, WDO employs additional terms in the velocity update equation (e.g., gravitation and Coriolis forces), providing robustness and extra degrees of freedom to fine tune. Along with the theory and terminology of WDO, a numerical study for tuning the WDO parameters is presented. WDO is further applied to three electromagnetics optimization problems, including the synthesis of a linear antenna array, a double-sided artificial magnetic conductor for WiFi applications, and an E-shaped microstrip patch antenna. These examples suggest that WDO can, in some cases, out-perform other well-known techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) or Differential Evolution (DE) and that WDO is well-suited for problems with both discrete and continuous-valued parameters. Link to Article

..: Website with Source Code :..

The Wind Driven Optimization (WDO) Algorithm
Developers:
Zikri Bayraktar, Ph.D. Electrical Engineering, Penn State
(Currently at Schlumberger-Doll Research Center)

Muge Komurcu Bayraktar, Ph.D. Meteorology, Penn State
(Currently at University of New Hampshire)

Prof. Douglas H. Werner, Ph.D. Electrical Engineering, Penn State
(Currently at Penn State)


..: References :..

[1]  Z. Bayraktar, M. Komurcu, and D. H. Werner, "Wind Driven Optimization (WDO): A Novel Nature-Inspired Optimization Algorithm and Its Application to Electromagnetics," Proceedings of the 2010 IEEE International Symposium on Antennas and Propagation and USNC/URSI National Radio Science Meeting, Toronto, Canada, July 11-17, 2010.

[2]  Z. Bayraktar, M. Komurcu, Z. Jiang, D. H. Werner, and P. L. Werner, "Stub-Loaded Inverted-F Antenna Synthesis via Wind Driven Optimization," Proceedings of the 2011 IEEE International Symposium on Antennas and Propagation and USNC/URSI National Radio Science Meeting, Spokane, WA, USA, July 3-8, 2011.

[3]  Z. Bayraktar, J. P. Turpin, and D. H. Werner, "Nature-Inspired Optimization of High-Impedance Metasurfaces with Ultra-Small Interwoven Unit Cells," IEEE Antennas and Wireless Propagation Letters, Vol. 10, pp 1563-1566, 2011.

[4]  Z. Bayraktar, M. Komurcu, and D. H. Werner, "Wind Driven Optimization Technique," Poster presented at the 2011 College of Engineering Research Symposium at the Pennsylvania State University, April 5, 2011.

[5]  Z. Bayraktar, M. Komurcu, and D. H. Werner, "A Novel Nature-inspired Numerical Optimization Technique," Poster presented at the 2011 Penn State University Graduate Exhibition, March 27, 2011.

[6]  K. Kuzu and Z. Bayraktar, "Wind Driven Optimization in Scheduling," 2012 INFORMS Annual Meeting, Phoenix, AZ, October 14-17, 2012.

[7]  Z. Bayraktar, M. Komurcu, J. A. Bossard and D. H. Werner, "The Wind Driven Optimization Technique and its Application in Electromagnetics," IEEE Transactions on Antennas and Propagation, Vol. 61, No. 5, pages 2745 - 2757, May 2013.

[8]  K. Kuzu, A. Ross, W. Li and Z. Bayraktar, "Wind Driven Optimization for Scheduling," 24th Annual POM Conference, Denver, CO, USA, May 3-6, 2013.

[9]  A. K. Bhandaria, V. K. Singha, A. Kumara, and G. K. Singh, "Cuckoo Search Algorithm and Wind Driven Optimization Based Study of Satellite Image Segmentation for Multilevel Thresholding Using Kapur's Entropy," Elsevier Expert Systems with Applications, 2013.

[10]  J. Sun, and X. Wang, M. Hung, and C. Gao, "A Cloud Resource Allocation Scheme Based on Microeconomics and Wind Driven Optimization," 8th ChinaGrid Annual Conference (ChinaGrid), Aug. 22-23, 2013.

[11]  Z. Bayraktar, "Novel Meta-surface Design Synthesis Via Nature-inspired Optimization Algorithms," Ph.D. Dissertation, The Pennsylvania State University, 2011.

[12]  Boulesnane, Abdennour, and Souham Meshoul, "A New Multi-region Modified Wind Driven Optimization Algorithm with Collision Avoidance for Dynamic Environments." Advances in Swarm Intelligence. Springer International Publishing, 2014. 412-421.

[13]  B. Kuldeepa, V.K. Singha, A. Kumara, and G.K. Singhb, "Design of two-channel filter bank using nature inspired optimization based fractional derivative constraints." ISA Transactions

[14]  S. K. Mahto, A. Choubey, and S. Suman, "Linear array synthesis with minimum side lobe level and null control using wind driven optimization," 2015 International Conference on Signal Processing And Communication Engineering Systems (SPACES).

[15]  Segundo, Emerson Hochsteiner de Vasconcelos, et al. "A Wind Driven Approach Using Levy Flights for Global Continuous Optimization." Artificial Intelligence, Modelling and Simulation (AIMS), 2014 2nd International Conference on. IEEE, 2014.

[16]  Zikri Bayraktar and Muge Komurcu, "Adaptive Wind Driven Optimization," Proceedings of the 9th EAI International Conference on Bio-Inspired Information and Communications Technologies (formerly BIONETICS), New York City, NY, Dec. 3-5, 2015.



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