The Wind Driven Optimization (WDO) Algorithm,
the Adaptive Wind Driven Optimization Algorithm (AWDO) and
the Multi-Objective Adaptive Wind Driven Optimization Algorithms (MOAWDO)


People | Algorithm | Codes | Publications | Links | Contact

Hello and welcome to the homepage of the Wind Driven Optimization Algorithm, i.e. the WDO. This page is created and maintained by Dr. Zikri Bayraktar. The WDO algorithm came to life as a side project during his graduate studies at the Pennsylvania State University and developed into a robust and efficient algorithm within few years. Dr. Bayraktar's initial idea of the wind moving from high pressure points to low pressure points nicely mapped to the optimization where we want to move from low performing combinations to high performing combinations within a search space. First publication on the WDO dates back to 2010, and we hope that the WDO will become the primary choice of many scientists, engineers, and anybody else involved in the field of optimization. We also hope that the potential of the WDO will be further explored and advanced through contribution of many. These are the reasons that the WDO is published here on these pages. Please send us a copy of your manuscript if you utilize the WDO in your scientific/technical paper or conference presentation. Thank you very much!

In December 2015, we developed and published the Adaptive Wind Driven Optimization (AWDO) algoritm to alleviate the need for tuning the internal parameters of the classical WDO by the user. The AWDO does not need its inherent terms to be set by the user. Terms in the velocity update equation, i.e. α, g, RT, c, will be selected by the AWDO at every iteration by itself. Please refer to Reference [16] for the Adaptive Wind Driven optimization algorithm.

In early 2016, we developed the Multi-objective Adaptive Wind Driven Optimization (MOAWDO) algoritm and submitted to international conferences for publication. Finally, we heard back in September 2016 and it will be presented in November 2016 [19].


Here is a list of people, who have been involved with the WDO since its invention.


The Wind Driven Optimization (WDO) algorithm is a new type of nature-inspired global optimization methodology based on 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. Along with the theory and terminology of WDO, a numerical study for tuning the WDO parameters is presented in [7]. WDO is further applied to electromagnetics optimization problems in [1]-[5], below. These examples suggest that WDO can, in some cases, out-perform other well-known techniques such as Particle Swarm Optimization (PSO) and that WDO is well-suited for problems with both discrete and continuous-valued parameters.

In your publications, please refer to [7] for detailed description of the Wind Driven Optimization algorithm, numerical parameter studies and real-world optimization problems.


Below is the list of WDO codes written in different programming languages. They are designed to be simple to provide a starting point to a new user of the WDO algorithm. If you have written your own version of the WDO in a different language that is not listed below and would like to share it with the rest of the world, please send us the code and your reference so that we can publish it here with your reference.

We hope to see the community around WDO and AWDO to grow over time.

Classical WDO Codes Adaptive WDO codes Multi-Objective Adaptive WDO codes


  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. Read it at IEEE Xplore.

  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. Read it at IEEE Xplore.

  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. Read it at IEEE Xplore.

  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, Volume 61, Issue 5, pages 2745 - 2757, May 2013. Read it at IEEE Xplore.

  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. Read it at ScienceDirect.

  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. Read it at IEEE Xplore.

  11. Z. Bayraktar, "Novel Meta-surface Design Synthesis Via Nature-inspired Optimization Algorithms," Ph.D. Dissertation, The Pennsylvania State University, 2011. Read it at Penn State eTD.

  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. Read it at Springer Link.

  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. Read it at ScienceDirect.

  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). Read it at IEEE Xplore.

  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. Read it at IEEE Xplore.

  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. Read it at ACM Digital Library. This publication was also selected to appear in the EAI Endorsed Transactions on Serious Games, Vol. 16, Number 8.

  17. Zikri Bayraktar, Michael Thiel and Dzevat Omeragic, "A two-step hybrid approach to 1-d formation inversion using adaptive wind driven optimization and Gauss-Newton method," 2016 Inverse Problems Symposium, June 5-7 2016, Lexington, VA.

  18. P. Di Barba, "Multi-objective Wind Driven Optimisation and Magnet Design," Electronics Letters, vol. 52, no. 14, pp.1216-1218, July 2016. Read it at IEEE Xplore.

  19. Zikri Bayraktar and Muge Komurcu, "Multi-objective Adaptive Wind Driven Optimization," Proceedings of the 8th International Conference on Evolutionary Computation Theory and Applications, Porto, Portugal, Nov 9-11, 2016. Read it at SciTePress.



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