Surveys, Benchmarking, Tutorials in Computational Intelligence

1.    P. N. Suganthan, "Numerical Optimization by DE and PSO", tutorial lecture at IEEE SDE/SIS/SSCI 2018, Bengaluru, India, 18th-21st Nov. 2018.

2.     G. Wu, R. Mallipeddi, P. N. Suganthan,Ensemble strategies for population-based optimization algorithms–A survey,” Swarm and Evolutionary Computation, https://doi.org/10.1016/j.swevo.2018.08.015 ,  2019

3.       P. N. Suganthan, On non-iterative learning algorithms with closed-form solution, Applied Soft Computing 70, 1078-1082, 2018.

4.    N Lynn, MZ Ali, PN Suganthan, Population topologies for particle swarm optimization and differential evolution, Swarm and Evolutionary Computation 39, 24-35, 2018.

5.       Mandavi, Sedigheh; Rahnamayan, Shahryar; Deb, Kalyanmoy, Opposition based learning: A literature review , Swarm and Evolutionary Computation,  Volume: 39   Pages: 1-23 ,  APR 2018 

6.    B.Y.Qu, Y.S.Zhu, Y.C.Jiao, M.Y.Wu, P.N.Suganthan, J.J.Liang, “A Survey on Multi-objective Evolutionary Algorithms for the Solution of the Environmental/Economic Dispatch Problems,” Swarm and Evolutionary Computation, Feb 2018.  https://doi.org/10.1016/j.swevo.2017.06.002

7.       L. Zhang, P. N. Suganthan, “Benchmarking Ensemble Classifiers with Novel Co-Trained Kernel Ridge Regression and Random Vector Functional Link Ensembles,” IEEE Computational Intelligence Magazine, Nov 2017.

8.    Rakshit, Pratyusha; Konar, Amit; Das, Swagatam, Noisy evolutionary optimization algorithms - A comprehensive survey , Swarm and Evolutionary Computation, Volume: 33   Pages: 18-45, APR 2017

9.    A. Rajasekhar, N. Lynn, S. Das, and P. N. Suganthan, "Computing with the Collective Intelligence of Honey Bees – A Survey," Swarm and Evolutionary Computation, pp. 25-48, Feb 2017. (supplementary file available here)  https://doi.org/10.1016/j.swevo.2016.06.001

10. Adam P. Piotrowski,Review of Differential Evolution population size,” Swarm and Evolutionary Computation, pp. 1-24, Feb 2017.

11. Emrah Hancer, Dervis Karaboga, A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number, pp. 49-67, Swarm and Evolutionary Computation, Feb 2017.

12. Akhilesh Gotmare, Sankha Subhra Bhattacharjee, Rohan Patidar, Nithin V. George,Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review,” pp. 68-84, Swarm and Evolutionary Computation, Feb 2017.

13. Ruchika Malhotra, Megha Khanna, Rajeev R. Raje,On the application of search-based techniques for software engineering predictive modeling: A systematic review and future directions, pp. 85-109, Swarm and Evolutionary Computation, Feb 2017.

14. Michalis Mavrovouniotis, Changhe Li, Shengxiang Yang,A survey of swarm intelligence for dynamic optimization: Algorithms and applications,” Swarm and Evolutionary Computation, pp. 1-17, April 2017.

15.  L. Zhang, P. N. Suganthan, "A Survey of Randomized Algorithms for Training Neural Networks," Information Sciences, DoI: 10.1016/j.ins.2016.01.039, October, 2016.  Also the editorial of this special issue on "Randomized Algorithms for Training Neural Networks" in Information Sciences. at  https://www.researchgate.net/publication/303392461_Editorial_Randomized_Algorithms_for_Training_Neural_Networks   by Dr Dianhui Wang.

16. S. Das, S. S. Mullick, P. N. Suganthan, "Recent Advances in Differential Evolution - An Updated Survey," Swarm and Evolutionary Computation, pp. 1-30, April 2016.  https://doi.org/10.1016/j.swevo.2016.01.004

17. Y. Ren, L. Zhang, and P. N. Suganthan, "Ensemble Classification and Regression – Recent Developments, Applications and Future Directions," IEEE Computational Intelligence Magazine, DOI: 10.1109/MCI.2015.2471235 , Feb 2016.

18. I Fister, K Ljubič, PN Suganthan, M Perc, "Computational intelligence in sports: Challenges and opportunities within a new research domain,"  Applied Mathematics and Computation 262, 178-186. 2015.

19. P. N. Suganthan, "Numerical Optimization by Nature Inspired Algorithms", keynote lecture at ICHSA 2015, Korea University, Seoul, 19th – 21st Aug. 2015.

20.  Musilek, P.; Kromer, P.; Barton, T., Review of nature-inspired methods for wake-up scheduling in wireless sensor networks ,  Swarm and Evolutionary Computation, Volume: 25   Special Issue: SI   Pages: 100-118   Published: DEC 2015.

21. Rammohan Mallipeddi, P. N. Suganthan, "Unit commitment – a survey and comparison of conventional and nature inspired algorithms", Int. J. Bio-Inspired Computation, Vol. 6, No. 2, 2014

22. S. J. Nanda, G. Panda, "A survey on nature inspired metaheuristic algorithms for partitional clustering", Swarm and Evolutionary Computation, Volume 16, June 2014, Pages 1–18. http://dx.doi.org/10.1016/j.swevo.2013.11.003

  1. Shafiq Alam, Gillian Dobbie, Yun Sing Koh, Patricia Riddle, Saeed Ur Rehman, "Research on particle swarm optimization based clustering: A systematic review of literature and techniques", Swarm and Evolutionary Computation, Volume 17, August 2014, Pages 1–13,  . http://dx.doi.org/10.1016/j.swevo.2014.02.001

24. M. Helbig, A. P. Engelbrecht, "Population-based metaheuristics for continuous boundary-constrained dynamic multi-objective optimisation problems," Swarm and Evolutionary Computation, Vol. 14, pp. 34–46, Feb 2014.

  1. I. Fister, I. Fister Jr., X-S. Yang, J. Brest, "A Comprehensive Review of Firefly Algorithms", Swarm and Evolutionary Computation, Vol. 13, pp. 34–46, Dec 2013.
  2. T. T. Nguyen, S. Yang, J. Branke, "Evolutionary dynamic optimization: A survey of the state of the art", Swarm and Evolutionary Computation, Vol. 6, Oct 2012, pp. 1–24.
  3. F. Neri, "Memetic Algorithms and Memetic Computing Optimization: A Literature Review", Vol. 2, pp. 1-12, Swarm and Evolutionary Computation, Feb 2012.
  4. E Mezura-Montes, C. A. Coello Coello, "Constraint-handling in nature-inspired numerical optimization: Past, present and future",  Vol. 1, No. 4, pp. 173-194, Swarm and Evolutionary Computation, Dec 2011.
  5. M. Hauschild, M. Pelikan, An introduction and survey of estimation of distribution algorithms , Vol. 1, No. 3, pp. 111-128, Swarm and Evolutionary Computation, Sept 2011.
  6. S. Das, S. Maity, B-Y Qu, P. N. Suganthan, "Real-parameter evolutionary multimodal optimization — A survey of the state-of-the-art", Vol. 1, No. 2,  pp. 71-88, Swarm and Evolutionary Computation, June 2011.
  7. Y. Jin, "Surrogate-assisted evolutionary computation: Recent advances and future challenges", Vol. 1, No. 2, pp. 61-70, Swarm and Evolutionary Computation, June 2011.
  8. P. N. Suganthan, S. Das, "Tutorial on Differential Evolution", SSCI-11 / SDE-11 Tutorial, Paris, France, April 2011.(part I, part II). 
  9. J. Derrac, S. García, D. Molina, F. Herrera, "A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms", Swarm and Evolutionary Computation, Vol. 1, No. 1, pp. 3-18, Mar 2011. 
  10.  A. E. Eiben, S. K. Smit, "Parameter tuning for configuring and analyzing evolutionary algorithms", Swarm and Evolutionary Computation, Vol. 1, No. 1, pp. 19-31, Mar 2011. 
  11. A. Zhou, B-Y. Qu, H. Li, S-Z. Zhao, P. N. Suganthan, Q. Zhang, "Multiobjective Evolutionary Algorithms: A Survey of the State-of-the-art", Swarm and Evolutionary Computation, Vol. 1, No. 1, pp. 32-49, Mar 2011.
  12. S. Das and P. N. Suganthan, "Differential Evolution: A Survey of the State-of-the-art", IEEE Trans. on Evolutionary Computation, Vol. 15, No. 1, pp. 4-31, Feb. 2011, DOI: 10.1109/TEVC.2010.2059031. (supplementary reference list cited as [Sxxx] is available for downloading).