Danial Yazdani

EMI Group 175
Name: Danial Yazdani
Position: Postdoctoral Fellow
Institution: Department of Computer Science and Engineering, SUSTech
Research Area: Evolutionary Computation, Data Science, Machine Learning, Simulation.
Contact: danial.yazdani@yahoo.com

 

Danial Yazdani received his PhD degree in Cmputer Science, Liverpool John Moores University, United Kingdom. He is a creative data/system analyzer and algorithm/simulator designer with 10+ years of research experience in academia. He is a prolific collaborator with proven ability to lead applied research projects involving industry and academic international partners resulting in 25+ peer-reviewed scientific publications. His main interests include dynamic and robust data-driven optimization problems, evolutionary computation, data-science, machine learning, simulation, and their applications in operational research and transportation/logistics.

Education

  • 2016-2018 Doctor of Philosophy (PhD) in Computational Intelligence, Liverpool John Moores University, United Kingdom.
  • 2008-2011 Software Engineering degree in computer science (MSc), Azad University, Qazvin Branch, Iran, GPA: 95.8%

Academic

  • 2019-present: Postdoctoral Research Fellow: Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), (China)
  • 2010-2015: Lecturer of Data Structures, Computer Simulation Design, Algorithm Design, Computer Programming, Advanced Topics in Computer Science, and Final Project, in Department of Computer Engineering and Information Technology, BIHD, (Iran).

Professional Activities

      1. Program Committee Member

  • Program Committee member, Evolutionary Algorithms and Meta-heuristics in Stochastic and Dynamic Environments (EvoSTOC), EvoStar 2017, Amsterdam.
  • Program Committee member, Evolutionary Algorithms and Meta-heuristics in Stochastic and Dynamic Environments(EvoSTOC), EvoStar 2018, Parma.   
  • Committee member, IEEE Computational Intelligence Society, Evolutionary Computation Technical Committee, Task Force on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE), 2019-now.

      2. Reviewer

  • IEEE Transactions on Cybernetics
  • IEEE Transactions on Evolutionary Computation  
  • IEEE Transactions on Emerging Topics in Computational Intelligence
  • Evolutionary Computation (MIT Press)
  • Applied Soft Computing
  • Neurocomputing
  • IEEE Congress on Evolutionary Computation (CEC)
  • European Conference on the Applications of Evolutionary Computation

Awards and Honours

  • SUSTech Presidential Outstanding Postdoctoral Award, 2019
  • Executive Deans Prize for Best Thesis, 2019
  • Nominated for the best paper at EvoStar, 2018

Publication

      1. Journals

  1. D. Yazdani, M. N. Omidvar, R Cheng*, J. Branke, T. Thanh Nguyen, and X. Yao. Benchmarking Continuous Dynamic Optimization: Survey and Generalized Test Suite. IEEE Transactions on Cybernetics, in Press.
  2. C. He, R. Cheng*, and D. Yazdani. Adaptive Offspring Generation for Evolutionary Large-Scale Multiobjective Optimization. IEEE Transactions on Systems, Man and Cybernetics: Systems, 2020.
  3. D. Yazdani, M. N. Omidvar, J. Branke, T. T. Nguyen, and X. Yao. Scaling up dynamic optimization problems: A divide-and-conquer approach. IEEE Transactions on Evolutionary Computation, 2019
  4. D. Yazdani, M. N. Omidvar, I. Deplano, C. Lersteau, A. Makki, J. Wang, and T. T. Nguyen. A new real-time seat allocation heuristic algorithm for railway systems. Transportation  Research Part C: Emerging Technologies, vol. 103, pp. 158–173, 2019
  5. D. Yazdani, T. T. Nguyen, and J. Branke. Robust optimization over time by learning problem space characteristics. IEEE Transactions on Evolutionary Computation, vol. 23, no. 1, pp. 143–155, 2019
  6. D. Yazdani, A. Sepas-Moghaddam, A. Dehban, and N. Horta. A novel approach for optimization in dynamic environments based on modified artificial fish swarm algorithm. International Journal of Computational Intelligence and Applications, vol. 15, no. 02, pp. 1650010–1650034, 2016
  7. D. Yazdani, B. Nasiri, A. Sepas-Moghaddam, M. R. Meybodi, and M. Akbarzadeh-Totonchi. mNAFSA: A novel approach for optimization in dynamic environments with global changes. Swarm and Evolutionary Computation, vol. 18, pp. 38–53, 2014
  8. D. Yazdani, B. Nasiri, R. Azizi, A. Sepas-Moghaddam, and M. R. Meybodi. Optimization in dynamic environments utilizing a novel method based on particle swarm optimization. International Journal of Artificial Intelligence, vol. 11, pp. 170–192, 2013
  9. D. Yazdani, B. Nasiri, A. Sepas-Moghaddam, and M. R. Meybodi. A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization. Applied Soft Computing, vol. 13, no. 04, pp. 2144–2158, 2013

     2. Conferences

  1. D. Yazdani, J. Branke, M. N. Omidvar, T. T. Nguyen, and X. Yao. Changing or keeping solutions in dynamic optimization problems with switching costs. Genetic and Evolutionary Computation Conference (GECCO). ACM, 2018, pp. 1095–1102
  2. D. Yazdani, T. T. Nguyen, J. Branke, and J. Wang. A multi-objective time-linkage approach for dynamic optimization problems with previous-solution displacement restriction. European Conference on the Applications of Evolutionary Computation, K. Sim and P. Kaufmann, Eds. Lecture Notes in Computer Science, 2018, vol. 10784, pp. 864–878
  3. D. Yazdani, T. T. Nguyen, J. Branke, and J. Wang. A new multi-swarm particle swarm optimization for robust optimization over time. Applications of Evolutionary Computation, G. Squillero and K. Sim, Eds. Springer Lecture Notes in Computer Science, 2017, vol. 10200, pp. 99–109
  4. D. Yazdani, A. Arabshahi, A. Sepas-Moghaddam, and M. M. Dehshibi. A multilevel thresholding method for image segmentation using a novel hybrid intelligent approach. International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2012, pp. 137–142
  5. D. Yazdani, M. R. Akbarzadeh-Totonchi, B. Nasiri, and M. R. Meybodi. A new artificial fish swarm algorithm for dynamic optimization problems. IEEE Congress on Evolutionary Computation (CEC). IEEE, 2012, pp. 1–8
  6. D. Yazdani, H. Nabizadeh, E. M. Kosari, and A. N. Toosi. Color quantization using modified artificial fish swarm algorithm. Advances in Artificial Intelligence, G. Squillero and K. Sim, Eds. Springer Lecture Notes in Computer Science, 2011, vol. 7106, pp. 382–391
  7. D. Yazdani, A. N. Toosi, and M. R. Meybodi. Fuzzy adaptive artificial fish swarm algorithm. Advances in Artificial Intelligence, J. Li, Ed. Springer Lecture Notes in Computer Science, 2010, vol. 6464, pp. 334–343
  8. D. Yazdani, S. Golyari, and M. R. Meybodi. A new hybrid approach for data clustering. International Symposium on Telecommunications (IST). IEEE, 2010, pp. 914–919