"The production of useful work is strictly limited by the laws of thermodynamics. The production of useless work seems to be unlimited."

— Donald E. Simanek (1936 - ),
US physicist, Educator, Humorist

1. Swagatam Das, "A free probabilistic framework for analyzing the transformer-based language models", Statistics and Probability Letters (Impact Factor 0.7), Vol. 226, November 2025, 110516.

2. Anish Chakrabarty, Arkaprabha Basu, and Swagatam Das, "Information preservation with Wasserstein autoencoders: generation consistency and adversarial robustness", Statistics and Computing, Vol. 35, Issue 114, https://doi.org/10.1007/s11222-025-10657-z, 2025.

3. Anish Chakrabarty, Sankha S. Mullick, and Swagatam Das, "Locally robust alignment between distinct spaces", Stat (Impact Factor 0.8), Wiley, Vol. 14, Issue 3, September 2025.

4. Srinjoy Roy, Subhajit Saha, and Swagatam Das, "From Wasserstein to maximum mean discrepancy barycenters: a novel framework for uncertainty propagation in model-free reinforcement learning", IEEE Transactions on Emerging Topics in Computational Intelligence (Impact factor 6.5), DOI: 10.1109/TETCI.2025.3593841, Accepted, 2025.

5. Václav Snášel, Lingping Kong, Swagatam Das, “From constraints fusion to manifold optimization: A new directional transport manifold metaheuristic algorithm”, Information Fusion (Impact Factor 14.7), Vol. 113, 102596, ISSN 1566-2535, https://doi.org/10.1016/j.inffus.2024.102596, 2025.

6. Priyobrata Mondal, Faizanuddin Ansari, and Swagatam Das, "CCO: A Cluster Core-Based Oversampling Technique for Improved Class-Imbalanced Learning," in IEEE Transactions on Emerging Topics in Computational Intelligence (Impact Factor 5.3), DOI: 10.1109/TETCI.2024.3407784, Accepted 2024.

7. Susmita Ghosh and Swagatam Das, “Multi-scale morphology-aided deep medical image segmentation”, Engineering Applications of Artificial Intelligence (Impact Factor 7.5), Vol. 137, Part A, 109047, ISSN 0952-1976, DOI: 10.1016/j.engappai.2024.109047, 2024.

8. Debolina Paul, Saptarshi Chakraborty, and Swagatam Das, "Robust principal component analysis: a median of means approach," IEEE Transactions on Neural Networks and Learning Systems (Impact Factor 10.4), vol. 35, no. 11, pp. 16788-16800, DOI: 10.1109/TNNLS.2023.3298011, 2024.

9. Pourya Shamsolmoali, Masoumeh Zareapoor, Swagatam Das, Eric Granger, and Salvador Garcia, "Hybrid Gromov-Wasserstein embedding for capsule learning", IEEE Transactions on Neural Networks and Learning Systems (Impact Factor 10.4), DOI: 10.1109/TNNLS.2023.3348657, Accepted, 2024.

10. Sandipan Dhar, Nanda Dulal Jana, and Swagatam Das, "GLGAN-VC: a guided loss based generative adversarial network for many-to-many voice conversion", IEEE Transactions on Neural Networks and Learning Systems (Impact Factor 10.4) 10.1109/TNNLS.2023.3335119, Accepted, 2023.

11. Debolina Paul, Saptarshi Chakraborty, Swagatam Das, and Jason Xu, "Implicit annealing in kernel spaces: a strongly consistent clustering approach," in IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact Factor 20.8), vol. 45, no. 5, pp. 5862-5871, 1 May 2023, doi: 10.1109/TPAMI. 2022.3217137.

12. Kushal Bose and Swagatam Das, "Can graph neural networks go deeper without over-smoothing? Yes, with a randomized path exploration!" in IEEE Transactions on Emerging Topics in Computational Intelligence (Impact Factor 5.3), vol. 7, no. 5, pp. 1595-1604, Oct. 2023, doi: 10.1109/TETCI.2023.3249255.

13. Abhishek Kumar, Swagatam Das, Lingping Kong, and Vaclav Snasel, “Self-adaptive spherical search with a low precision projection matrix for real-world optimization”, IEEE Transactions on Cybernetics, (Impact Factor: 19.118), vol. 53, no. 7, pp. 4107 - 4121, July 2023, doi: 10.1109/TCYB.2021.3119386 .

14. Saptarshi Chakraborty, Debolina Paul, and Swagatam Das, "On consistent entropy-regularized k-means clustering with feature weight learning: algorithm and statistical analyses," in IEEE Transactions on Cybernetics (Impact Factor: 19.118), Vol. 53, no. 8, pp. 4779-4790, Aug. 2023, doi: 10.1109/TCYB.2022.3166975.

15. Swagatam Das, Sankha S. Mullick, and Ivan Zelinka, "On supervised class-imbalanced learning: an updated perspective and some key challenges", IEEE Transactions on Artificial Intelligence, DOI: 10.1109/TAI.2022.3160658, vol. 3, no. 6, pp. 973-993, Dec. 2022.

16. Abhishek Kumar, Swagatam Das, and Rammohan Mallipeddi, "An efficient differential grouping algorithm for large-scale global optimization", IEEE Transactions on Evolutionary Computation, (Impact Factor: 11.7), vol. 28, no. 1, pp: 32-46, DOI: 10.1109/TEVC.2022.3230070, 2022.

17. Surbhi Gupta, Gaurav Singal, Deepak Garg, and Swagatam Das, "RSAC: a robust deep reinforcement learning strategy for dimensionality perturbation", IEEE Transactions on Emerging Topics in Computational Intelligence (Impact Factor: 5.3), DOI:10.1109/TETCI.2022.3157003, vol. 6, no. 5, pp. 1157-1166, Oct. 2022.

18. Avisek Gupta and Swagatam Das, "Transfer clustering using a multiple kernel metric learned under multi-instance weak supervision", IEEE Transactions on Emerging Topics in Computational Intelligence (Impact Factor: 5.3), DOI: 10.1109/TETCI.2021.3110526, vol. 6, no. 4, pp. 828-838, Aug. 2022.

19. Abhishek Kumar, Swagatam Das, Rakesh Kumar Misra, and Devender Singh, "A $\upsilon$-constrained matrix adaptation evolution strategy with Broyden-based mutation for constrained optimization", IEEE Transactions on Cybernetics (Impact Factor: 19.118), DOI: 10.1109/TCYB.2020.3042853, vol. 52, no. 6, pp. 4784-4796, June 2022.

20. Abhishek Kumar, Swagatam Das, and Rammohan Mallipeddi, “A reference vector based simplified covariance matrix adaptation evolution strategy for constrained global optimization”, IEEE Transactions on Cybernetics (Impact Factor: 19.118), DOI: 10.1109/TCYB.2020.3013950, vol. 52, no. 5, pp. 3696 - 3709, May 2022.

21. Arkaprabha Basu, Sourav Das, Sankha S. Mullick, and Swagatam Das, "Do preprocessing and class imbalance matter to the deep image classifiers for COVID-19 detection? an explainable analysis," in IEEE Transactions on Artificial Intelligence, vol. 4, no. 2, pp. 229-241, April 2023, doi: 10.1109/TAI.2022.3149971.

22. Avisek Gupta and Swagatam Das, "On efficient model selection for sparse hard and fuzzy center-based clustering algorithms", Information Sciences (Impact Factor 8.233), Vol. 590: 29-44, 2022.

23. Souhardya Sengupta and Swagatam Das, "Selective nearest neighbors clustering", Pattern Recognition Letters (Impact Factor 4.757), Vol. 155, pp. 178-185, 2022.

24. Anish Chakrabarty and Swagatam Das, "On strong consistency of kernel k-means: a Rademacher complexity approach", Statistics and Probability Letters (Impact Factor 0.87), Elsevier, https://doi.org/10.1016/j.spl.2021.109291, Vol. 182, 109291, 2022.

25. Saptarshi Chakraborty and Swagatam Das, “On uniform concentration bounds for bi-clustering by using the Vapnik–Chervonenkis theory”, Statistics and Probability Letters, (Impact Factor 0.87) Volume 175, 2021, 109102, https://doi.org/10.1016/j.spl.2021.109102.

26. Debolina Paul, Saptarshi Chakraborty, and Swagatam Das, "On the uniform concentration bounds and large sample properties of clustering with Bregman divergences", Stat (Impact Factor: 2.451), Wiley, Vol. 10:e360, https://doi.org/10.1002/sta4.360, 2021.

27. Arka Ghosh, Sankha Subhra Mullick, Shounak Datta, Swagatam Das, Asit Kr. Das, Rammohan Mallipeddi, “A black-box adversarial attack strategy with adjustable sparsity and generalizability for deep image classifiers”, Pattern Recognition (Impact Factor: 8.518), https://doi.org/10.1016/j.patcog.2021.108279, Vol. 122, 108279, Feb. 2022.

28. 20.   Abhishek Kumar, Swagatam Das, and Rammohan Mallipeddi, "An inversion-free robust power flow algorithm for microgrids ", IEEE Transactions on Smart Grid (Impact Factor: 10.275), DOI: 10.1109/TSG.2021.3064656, vol. 12, no. 4, pp. 2844-2859, July 2021.

29. Avisek Gupta, Shounak Datta, and Swagatam Das, "Fuzzy clustering to identify clusters at different levels of fuzziness: an evolutionary multiobjective optimization approach", IEEE Transactions on Cybernetics (Impact Factor: 19.118), DOI: 10.1109/TCYB.2019.2907002, Vol. 51(5), pp. 2601 – 2611, 2021.

30. A. Hanif Halim, Idris Ismail, and Swagatam Das, "Performance assessment of the metaheuristic optimization algorithms: an exhaustive review", Artificial Intelligence Review (Impact Factor 9.588), Springer, Vol. 54(3), pp. 2323 – 2409, 2021.

31. Yi Chen, Aimin Zhou, and Swagatam Das, "Utilizing dependence among variables in evolutionary algorithms for mixed-integer programming: A case study on multi-objective constrained portfolio optimization", Swarm and Evolutionary Computation (Impact Factor 10.267), Volume 66, 2021.

32. Debolina Paul and Swagatam Das, "A Bayesian non-parametric approach for automatic clustering with feature weighting", Stat, Wilely (Impact Factor 2.451), Vol. https://doi.org/10.1002/sta4.306, 2020.

33. Saptarshi Chakraborty, Debolina Paul and Swagatam Das, "Hierarchical clustering with optimal transport", Statistics & Probability Letters (Impact Factor 0.680), Vol. 163, 2020, https://doi.org/10.1016/j.spl.2020.108781.

34. Alenrex Maity and Swagatam Das, "Efficient hybrid local search heuristics for solving the travelling thief problem", Applied Soft Computing (Impact Factor 5.472), Vol. 93, 2020, 106284, https://doi.org/10.1016/j.asoc.2020.106284.

35. Arka Ghosh, Swagatam Das, Asit Kr. Das, and Liang Gao, "Reusing the past difference vectors in differential evolution - a simple but significant improvement", IEEE Transactions on Cybernetics (Impact Factor: 19.118), DOI: 10.1109/TCYB.2019.2921602, vol. 50, no. 11, pp. 4821-4834, Nov. 2020.

36. Shounak Datta, Sayak Nag and Swagatam Das, "Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost Tuning", IEEE Transactions on Knowledge and Data Engineering (Impact Factor 9.235), Vol. 32, Issue 5, Page(s): 883 - 897, DOI: 10.1109/TKDE.2019.2894148, May, 2020.

37. Sankha Subhra Mullick, Shounak Datta, Sourish Gunesh Dhekane and Swagatam Das, "Appropriateness of performance indices for imbalanced data classification: an analysis", Pattern Recognition (Impact Factor: 8.518), Volume 102, 107197, DOI: https://doi.org/10.1016/j.patcog.2020.107197, June 2020.

38. 31. Arkajyoti Saha and Swagatam Das, "Stronger convergence results for the center-based fuzzy clustering with convex divergence measure", IEEE Transactions on Cybernetics (Impact Factor 19.118), DOI: 10.1109/TCYB.2018.2861211, vol. 49, no. 12, pp. 4229-4242, Dec. 2019.

39. Deepayan Sanyal and Swagatam Das, “On semi-supervised active clustering of stable instances with oracles”, Information Processing Letters (Impact Factor 0.851), Vol. 151, 2019, 105833, https://doi.org/10.1016/j.ipl.2019.105833.

40. Kunkun Peng, Quan-Ke Pan, Liang Gao, Xinyu Li, Swagatam Das, and Biao Zhang, “A multi-start variable neighbourhood descent algorithm for hybrid flow-shop rescheduling”, Swarm and Evolutionary Computation (Impact Factor 10.267), Vol. 45, pp. 92 – 112, 2019.

41. Saptarshi Chakraborty and Swagatam Das, "On the strong consistency of feature weighted k-means clustering in a nearmetric space", Stat, Wiley (Impact Factor 2.451) DOI: 10.1002/sta4.227, 2019.

42. Imon Banerjee, Sankha Subhra Mullick and Swagatam Das, "On convergence of the class membership estimator in fuzzy k-Nearest neighbor classifier", IEEE Transactions on Fuzzy Systems (Impact Factor 12.253), DOI: 10.1109/TFUZZ.2018.2874017, vol. 27, no. 6, pp. 1226-1236, June 2019.

43. Shounak Datta and Swagatam Das, "Multi-objective support vector machines: handling class imbalance with Pareto optimality,” IEEE Transactions on Neural Networks and Learning Systems (Impact Factor 14.255), DOI: 10.1109/TNNLS.2018.2869298, vol. 30, no. 5, pp. 1602-1608, May 2019.

44. Avisek Gupta, Shounak Datta, and Swagatam Das, "Fast automatic estimation of the number of clusters from the minimum inter-center distance for k-Means clustering", Pattern Recognition Letters (Impact Factor 4.757), Vol. 116, Pages 72-79, December 2018.

45. Shounak Datta, Supritam Bhattachrjee and Swagatam Das, "Clustering with missing features: a penalized dissimilarity measure based approach", Machine Learning (Impact Factor 1.848), Springer, DOI: 0.1007/s10994-018-5722-4, Vol. 107, Issue 12, pp 1987–2025, 2018.

46. Sankha Subhra Mullick, Shounak Datta and Swagatam Das, "Adaptive learning based k-nearest neighbor classifiers with resilience to class imbalance", IEEE Transactions on Neural Networks and Learning Systems (Impact Factor 6.108), 29.11, pp: 5713-5725, 2018, DOI: 10.1109/TNNLS.2018.2812279.

47. Swagatam Das, Shounak Datta, and B. B. Chaudhuri, "Handling data irregularities in classification: foundations, trends, and future challenges", Pattern Recognition (Impact Factor 3.962), Vol. 81, Pages 674-693, September 2018.

48. Saptarshi Chakraborty and Swagatam Das, "Simultaneous variable weighting and determining the number of clusters - A weighted Gaussian means algorithm", Statistics and Probability Letters (Impact Factor 0.533), Vol. 137, Pages 148-156, June 2018.

49. Arkajyoti Saha and Swagatam Das, "Clustering of fuzzy data and simultaneous feature selection: A model selection approach", Fuzzy Sets and Systems (Impact Factor 2.675), Vol. 340, Pages 1-37, June 2018.

50. Nimagna Biswas, Saurajit Chakraborty, Sankha Subhra Mullick, and Swagatam Das, "A parameter independent fuzzy weighted k-nearest neighbor classifier", Pattern Recognition Letters (Impact Factor 1.952), Vol. 101, Pages 80-87, January 2018.

51. Arka Ghosh, Swagatam Das, Rammohan Mallipeddi, Asit Kr. Das and Subhransu S. Dash, "A modified differential evolution with distance-based selection for continuous optimization in presence of noise", IEEE Access (Impact Factor 3.557), DOI:10.1109/ACCESS.2017.2773825, Pages 26944-26964, 2017.

52. Saptarshi Chakraborty and Swagatam Das, "k-Means clustering with a new divergence-based distance metric: convergence and performance analysis", Pattern Recognition Letters (Impact Factor 4.757), Vol. 100, pp. 67-73, 2017.

53. Arkajyoti Saha and Swagatam Das, "On the unification of possibilistic fuzzy clustering: Axiomatic development and convergence analysis", Fuzzy Sets and Systems (Impact Factor 2.718), vol. 340, pp: 73-90, 2018, DOI: https://doi.org/10.1016/j.fss.2017.07.005.

54. Arkajyoti Saha and Swagatam Das, "Feature weighted clustering with inner product induced norm based dissimilarity measures: an optimization perspective", Machine Learning (Impact Factor 1.719), Springer, Vol. 106, Issue 7, pp 951-992, 2017.

55. Shounak Datta, Sankha Subhra Mullick, and Swagatam Das, "Generalized mean-based back-propagation of errors for ambiguity resolution", Pattern Recognition Letters (Impact Factor 4.757), Vol. 94, Pages 22-29, 2017.

56. Ayan Das and Swagatam Das, "Feature weighting and selection with a Pareto-optimal trade-off between relevancy and redundancy", Pattern Recognition Letters (Impact Factor 4.757), Vol. 88, pp. 12-19, 2017.

57. Arkajyoti Saha and Swagatam Das, "Axiomatic generalization of the membership degree based weighting function for Fuzzy C Means clustering: theoretical development and convergence analysis", Information Sciences (Impact Factor: 4.832), Vol. 408, Pages 129-145, 2017.

58. Shounak Datta, Abhiroop Ghosh, Krishnendu Sanyal, and Swagatam Das, "A radial boundary intersection aided interior point method for multi-objective optimization", Information Sciences (Impact Factor 3.364), Vol. 377, Pages 1-16, 20 Jan. 2017.

59. Chiranjib Saha, Swagatam Das, Kunal Pal, and Satrajit Mukherjee, "Fuzzy rule-based penalty function approach for constrained optimization", IEEE Transactions on Cybenetics, (Impact Factor 4.943), Vol. 46, No. 12, pp. 2953-2965, 2016.

60. Arkajyoti Saha and Swagatam Das, "Optimizing cluster structures with inner product induced norm-based dissimilarity measures: theoretical development and convergence analysis", Information Sciences, (Impact Factor 3.364), vol. 372, pp: 796-814, 2016, DOI:10.1016/j.ins.2016.08.058.

61. Shounak Datta, Debaleena Misra and Swagatam Das, "A feature weighted penalty based dissimilarity measure for k-Nearest neighbor classification with missing features", Pattern Recognition Letters, (Impact Factor 1.586), Vol. 80, Pages 231-237, Sept., 2016.

62. Arkajyoti Saha and Swagatam Das, "Geometric divergence based fuzzy clustering with strong resilience to noise features", Pattern Recognition Letters, (Impact Factor 1.586), Vol. 79, Pages 60-67, 1 August, 2016.

63. Rohan Mukherjee, Shantanab Debchoudhury and Swagatam Das, "Modified differential evolution with locality induced genetic operators for dynamic optimization", European Journal of Operational Research, (Impact Factor 2.358), Vol. 253, No. 2, Pages 337-355, 2016.

64. Deep Kiran, B. K Panigrahi, Swagatam Das, and Nitesh Kumar, "Linkage-based deferred acceptance optimization", Information Sciences, (Impact Factor 4.038), Volumes 349 - 350, Pages 65 - 76, 2016.

65. Swagatam Das, Sankha S. Mullick and P. N. Suganthan, "Recent advances in differential evolution - an updated survey", Swarm and Evolutionary Computation (Impact Factor 3.818), Elsevier, Vol. 27, Pages 1-30, 2016.

66.  Shounak Datta and Swagatam Das, "Near-Bayesian support vector machines for imbalanced data classification with equal or unequal misclassification costs", Neural Networks (Elsevier), Vol. 70, pp. 39-52, (Impact Factor: 2.708), 2015.

67. Sujoy Paul and Swagatam Das, "Simultaneous feature selection and weighting - an evolutionary multi-objective optimization approach", Pattern Recognition Letters (Impact Factor: 1.551), Vol. 65, 1 Pages 51-59, 2015.

68.  Swagatam Das, "Chaotic patterns in the discrete time dynamics of social foraging swarms with attractant-repellent profiles - an analysis", Nonlinear Dynamics, DOI: 10.1007/s11071-015-2247-2, Springer, (Impact Factor 2.849), Vo. 82, pages 1399-1417, 2015.

69. Arkajyoti Saha and Swagatam Das, "Automated feature weighting in clustering with separable distances and inner product induced norms - a theoretical generalization", Pattern Recognition Letters (Impact Factor 1.551, h-5 index 46), Vol. 63, Pages 50-58, 2015.

70. Subhodip Biswas, Souvik Kundu, and Swagatam Das, "Inducing niching behavior in differential evolution through local information sharing", IEEE Transactions on Evolutionary Computation (Impact Factor 5.545), Vol. 19, Issue 2, pp. 246-263, 2015.

71. Arkajyoti Saha and Swagatam Das, "Categorical fuzzy k-modes clustering with automated feature weight learning", Neurocomputing, (Impact Factor: 2.005), Vol.166, pp. 422-435, 2015.

72. Preetam Dasgupta and Swagatam Das, “A discrete inter-species cuckoo search for flowshop scheduling problems", Computers & Operations Research, (Impact Factor: 1.718), Vol. 60, Pages 111-120, August 2015.

73. Soham Sarkar, Swagatam Das, and Sheli Sinha Choudhury, "A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution", Pattern Recognition Letters (Impact Factor 1.062), Vol. 54, pp 27-35, 2015.

74. Swagatam Das, Subhodip Biswas, B. K. Panigrahi, Souvik Kundu, and Debabrota Basu, "A spatially informative optic flow model of bee colony with saccadic flight strategy for global optimization", IEEE Transactions on Cybernetics (Impact Factor 3.08), Vol. 44, No. 10, pp. 1884-1897, 2014.

75. 72. Miguel A. Medina, Swagatam Das, Carlos A. Coello Coello, and Juan M. Ramírez, "Decomposition-based modern metaheuristic algorithms for multi-objective optimal power flow - a comparative study", Engineering Applications of Artificial Intelligence (Impact Factor 1.625), Vol. 32, pp. 10-20, 2014.

76. Faruk Ahmed and Swagatam Das, "Removal of high density salt-and-pepper noise in images with an iterative adaptive fuzzy filter using alpha-trimmed mean", IEEE Transactions on Fuzzy Systems (Impact Factor 6.306), Vol. 22, No. 5, pp. 1352-1358, 2014.

77. Subhodip Biswas, Souvik Kundu, and Swagatam Das, "An improved parent-centric mutation with normalized neighborhoods for inducing niching behavior in differential evolution", IEEE Transactions on Cybernetics (2011 Impact Factor 3.08), Vol. 44, No. 10, pp. 1726-1737, 2014.

78. Soham Sarkar and Swagatam Das, “Multi-level image thresholding based on two-dimensional histogram and maximum tsallis entropy - a differential evolution approach”, IEEE Transactions on Image Processing (Impact Factor 3.199), Vol. 22, Issue 12, pp. 4788-4797, 2013.

79. Swagatam Das, Ankush Mandal, and Rohan Mukherjee, “An adaptive differential evolution algorithm for global optimization in dynamic environments”, IEEE Transactions on Cybernetics (2011 Impact Factor 3.08), Vol. 44, No. 6, pp. 966-978, 2014.

80. Pratyusha Rakshit, Amit Konar, Swagatam Das, Lakhmi C. Jain, and Atulya K. Nagar,  "Uncertainty management in differential evolution induced multi-objective optimization in presence of measurement noise",  IEEE Transactions on Systems, Man and Cybernetics: Systems (2011 Impact Factor 2.123),Vol. 44, No. 7, pp. 922-937, 2014.

81. Aniruddha Basak, Swagatam Das, and Kay Chen Tan, "Multimodal optimization using a bi-objective differential evolution algorithm enhanced with mean distance based selection", IEEE Transactions on Evolutionary Computation (Impact Factor 4.81), Vol. 17, No. 5, Sept. 2013.

82. Swagatam Das, Subhodip Biswas, and Souvik Kundu, “Synergizing fitness learning with proximity-based food source selection in artificial bee colony algorithm for numerical optimization”, Applied Soft Computing (Impact Factor 2.140), Vol. 13, Issue 12, Dec. 2013, Pages 4676 - 4694.

83. Pratyusha Rakshit, Amit Konar, Pavel Bhowmik, Swagatam Das, Lakhmi C. Jain, and Atulya K. Nagar, "Realization of an adaptive memetic algorithm using differential evolution and Q-learning: a case study in multi-robot path-planning", IEEE Transactions on Systems, Man and Cybernetics, Part - A, (Impact Factor 2.123), vol. 43, no. 4, pp. 814-831, July 2013, doi: 10.1109/TSMCA.2012.2226024.

84. Udit Halder, Swagatam Das, and Dipankar Maity, "A cluster-based differential evolution algorithm with external archive for optimization in dynamic environments", IEEE Transactions on Systems, Man, and Cybernetics, Part – B (Impact Factor 3.08), vol. 43, no. 3, pp. 881-897, June 2013, doi: 10.1109/TSMCB.2012.2217491.

85. Md. Nasir, Swagatam Das, Soumyadip Sengupta, Athanasios V. Vasilakos, and Witold Pedrycz, “An evolutionary multi-objective sleep scheduling scheme for differentiated coverage in wireless sensor networks”, IEEE Transactions on Systems, Man and Cybernetics (SMC) Part – C, (Impact Factor 2.105), Vol. 42(6): 1093-1102 (2012).

86. Pradipta Ghosh, Swagatam Das, and Hamim Zafar, "Adaptive differential evolution based design of two-channel quadrature mirror filter banks for sub-band coding and data transmission", IEEE Transactions on Systems, Man, and Cybernetics, Part - C, (Impact Factor 2.105), Vol.  42(6): 1613-1623 (2012).

87. Swagatam Das, Udit Halder, and Dipankar Maity, “Chaotic dynamic characteristics of social foraging swarms – an analysis”, IEEE Transactions on Systems, Man and Cybernetics (SMC) Part – B (Impact Factor 3.08), Vol 42, No. 4, pp. 1288 - 1293, Aug. 2012.

88. Sk. Minhazul Islam, Swagatam Das, Saurav Ghosh, Subhrajit Roy, and P. N. Suganthan, “An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization”, IEEE Transactions on Systems, Man and Cybernetics (SMC) Part – B (Impact Factor 3.08), 42(2): 482 -500, 2012.

89. Sayan Ghosh, Swagatam Das, Athanasios V. Vasilakos, and Kaushik Suresh, “On convergence of differential evolution over a class of continuous functions with unique global optimum”, IEEE Transactions on Systems, Man and Cybernetics (SMC) Part – B, (Impact Factor 3.08), 42(1): 107-124, 2012.

90. 103.  Abhishek Sinha, Swagatam Das, and B. K. Panigrahi, “On some properties of the island biogeography system”, IEEE Transactions on Systems, Man and Cybernetics (SMC), Part – A (Impact Factor 2.1), Vol. 41, No. 2, pp 331 – 337, 2011.

91. Swagatam Das, Arpan Mukhopadhyay, Anwith Roy, Ajith Abraham, and B. K. Panigrahi, “On exploratory power of the harmony search algorithm: analysis and improvements for global numerical optimization”, IEEE Transactions on Systems, Man and Cybernetics (SMC) Part – B (Impact Factor 3.08), Vol. 41, No. 1, pp. 89 – 106, 2011.

92. Swagatam Das and P. N. Suganthan, “Differential evolution – a survey of the state-of-the-art”, IEEE Transactions on Evolutionary Computation (Impact Factor 3.341), Vol. 15, No. 1, pp. 4 – 31, Feb. 2011.

93. Gourab Ghosh Roy, Swagatam Das, Prithwish Chakraborty, and P. N. Suganthan, “Design of non-uniform circular antenna arrays using a modified invasive weed optimization algorithm”, IEEE Transactions on Antenna and Propagation (Impact Factor 2.151), Vol. 59, No. 1, pp. 110 – 118, Jan. 2011.

94. Arijit Biswas, Sambarta Dasgupta, Swagatam Das, and Ajith Abraham, “Dynamics of reproduction in artificial bacterial foraging system: modeling and stability analysis,” Theoretical Computer Science (Impact Factor 0.838), 411(21), pp. 2127-2139 Elsevier Science, 2010.

95. Swagatam Das, Sambarta Dasgupta, Arijit Biswas, Ajith Abraham, and Amit Konar, “On stability of chemotactic dynamics in bacterial foraging optimization algorithm”, IEEE Transactions on Systems, Man, and Cybernetics, Part – A (Impact Factor 2.1), Vol. 39, Issue 3, pp. 670 – 679, 2009.

96.  Swagatam Das, Amit Konar, Uday K. Chakraborty, and Ajith Abraham, “Differential evolution with a neighborhood based mutation operator: a comparative study”, IEEE Transactions on Evolutionary Computation (Impact Factor 3.341), Vol. 13, Issue 3, Page(s): 526-553, June, 2009.

97. Sambarta Dasgupta, Swagatam Das, Ajith Abraham, and Arijit Biswas, “Adaptive computational chemotaxis in bacterial foraging optimization: an analysis”, IEEE Transactions on Evolutionary Computation (Impact Factor 3.341), Vol. 13, Issue 4, Page(s): 919 – 941, 2009.

98. Swagatam Das, Arijit Biswas, Ajith Abraham and Sambarta Dasgupta, “Design of fractional order PIλDμ  controllers with an improved differential evolution”, Engineering Applications of Artificial Intelligence (Impact Factor 1.844), Elsevier Science, Vol. 22, Issue 2, Pages 343-350, March 2009.

99. Swagatam Das, Ajith Abraham, and Amit Konar, “Automatic clustering using an improved differential evolution algorithm”, IEEE Transactions on Systems Man and Cybernetics - Part A (Impact factor 2.1), Vol. 38, No. 1, January 2008, pp. 218 - 237.

100.  Swagatam Das and Amit Konar, “A swarm intelligence approach to the synthesis of two-dimensional IIR filters”, Engineering Applications of Artificial Intelligence, (Impact Factor 1.844), Elsevier Science, Vol. 20, Issue 8, 2007, pp.1023-1162.