Danial Yazdani

Name: Danial Yazdani
Position: Postdoctoral Fellow
Institution: Department of Computer Science and Engineering, SUSTech
Research Area: Dynamic optimization problems,

simulation

Contact: danial.yazdani@gmail.com

 

Danial received his PhD degree in Computational Intelligence, Liverpool John Moores University, United Kingdom. He is a creative algorithm designer and problem solver with 10+ years of research experience in academia strong background in optimization leading to successful development of algorithms and publications for optimization, especially dynamic optimization problems. He is a prolific collaborator and an excellent team player with proven ability to lead applied research projects involving industry and academic international partners resulting in 20+ peer-reviewed scientific publications. His main interests include dynamic optimization problems (dynamic constrained optimization problems, robust optimization over time, dynamic multi-objective optimization problems, large-scale dynamic optimization problems, time-linkage dynamic optimization problems, combinatorial dynamic optimization problems), and simulation: multi-agent based simulation, discrete event based simulation.

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%
  • 2002-2007 Software Engineering degree in computer science (BSc), Azad University, Shirvan Branch, Iran, GPA: 73.55%

Academic

  • 2019-present: Postdoctoral Research Fellow: Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), (China)
  • 2010-2015: Supervised Thesis : Computer engineering and Information technology group, (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.   

      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. 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.
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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