+91-11- 24195298

jcbansal@sau.ac.in

Associate Professor

South Asian University New Delhi, India

Dr. Jagdish Chand Bansal is an Associate Professor at South Asian University New Delhi and Visiting Faculty at Maths and Computer Science, Liverpool Hope University UK. Dr. Bansal has obtained his Ph.D. in Mathematics from IIT Roorkee. Before joining SAU New Delhi he has worked as an Assistant Professor at ABV- Indian Institute of Information Technology and Management Gwalior and BITS Pilani. His Primary area of interest is Swarm Intelligence and Nature Inspired Optimization Techniques. Recently, he proposed a fission-fusion social structure based optimization algorithm, Spider Monkey Optimization (SMO), which is being applied to various problems from engineering domain. He has published more than 70 research papers in various international journals/conferences. He has worked with Indian Air Force and Liverpool Hope University UK for various researh projects. He is the editor in chief of the journal MethodsX published by Elsevier. He is the series editor of the book series Algorithms for Intelligent Systems (AIS) and Studies in Autonomic, Data-driven and Industrial Computing (SADIC) published by Springer. He is the editor in chief of International Journal of Swarm Intelligence (IJSI) published by Inderscience. He is also the Associate Editor of IEEE ACESSS published by IEEE and ARRAY published by Elsevier. He is the general secretary of Soft Computing Research Society (SCRS). He has also received Gold Medal at UG and PG level.

Research Specialization: 

Optimization; Computational Intelligence; Swarm Intelligence; Soft Computing

Research Profiles

      

 


Selected Journal Publications

(Scroll to see the full list)

44

Sakshi Shringi, Harish Sharma, Pushpa Narayan Rathie, Jagdish Chand Bansal, Atulya Nagar, Modified SIRD Model for COVID-19 Spread Prediction for Northern and Southern States of India , Chaos, Solitons and Fractals, In Press Impact Factor- 5.944

43

Chaudhary, Vishal, Hari Mohan Dubey, Manjaree Pandit, and Jagdish Chand Bansal. "Multi-area economic dispatch with stochastic wind power using salp swarm algorithm." Array 8 (2020): 100044.

42

Susheel Kumar Joshi, Anshul Gopal, Shitu Singh, Atulya K. Nagar, Jagdish Chand Bansal, "A novel neighbourhood archives embedded gravitational constant in GSA" Soft Computing | Impact Factor 3.643

41

Jagdish Chand Bansal, Shitu Singh: "A better exploration strategy in Grey Wolf Optimizer", Journal of Ambient Intelligence and Humanized Computing | Impact factor 7.104

DOI https://doi.org/10.1007/s12652-020-02153-1

40

Susheel Kumar Joshi, Jagdish Chand Bansal: "Parameter Tuning for Meta-heuristics", Knowledge-Based Systems, 189 (2020): 105094 | Impact Factor 8.038

https://doi.org/10.1016/j.knosys.2019.105094 

39

Anshul Gopal, Mohammad Mahdi Sultani, Jagdish Chand Bansal: "On Stability Analysis of Particle Swarm Optimization Algorithm", Arabian Journal for Science and Engineering  45, 2385–2394 (2020) | Impact Factor: 2.334

https://doi.org/10.1007/s13369-019-03991-8

38

Soniya Lalwani, Harish Sharma, Suresh Chandra Satapathy, Kusum Deep, Jagdish Chand Bansal: "A Survey on Parallel Particle Swarm Optimization Algorithms", Arabian Journal for Science and Engineering, 44, 2899–2923 (2019) | Impact Factor: 2.334

https://doi.org/10.1007/s13369-018-03713-6

37

Jagdish Chand Bansal, Anshul Gopal, Atulya K Nagar: "Analysing convergence, consistency and trajectory of Artificial Bee Colony Algorithm", IEEE Access vol. 6, pp. 73593-73602, 2018 | Impact Factor: 3.367

10.1109/ACCESS.2018.2884255 

36

Sandeep Kumar, Basudev Sharma, Vivek Kumar Sharma, Harish Sharma, Jagdish Chand Bansal: "Plant Leaf Disease Identification using Exponential Spider Monkey Optimization", Sustainable Computing-Informatics and Systems, 2018 | Impact Factor: 4.028

https://doi.org/10.1016/j.suscom.2018.10.004

35

Nirmala Sharmaa, Harish Sharma, Ajay Sharma, and Jagdish Chand Bansal: "Grasshopper inspired artificial bee colony algorithm for numerical optimization", Journal of Experimental & Theoretical Artificial Intelligence, 2018 | Impact Factor: 2.340

https://doi.org/10.1080/0952813X.2018.1552317

34

Pushpa Farswan, Jagdish Chand Bansal: "Fireworks-inspired biogeography-based optimization", Soft Computing, 23, pages7091–7115(2019) | Impact Factor: 3.643

https://doi.org/10.1007/s00500-018-3351-2 

33

Jagdish Chand Bansal, Pushpa Farswan, Atulya Kumar Nagar: "Design of wind farm layout with non-uniform turbines using fitness difference based BBO", Engineering Applications of Artificial Intelligence, Volume 71, Pages 45-59, 2018 | Impact Factor: 6.212

https://doi.org/10.1016/j.engappai.2018.02.007

32

Jagdish Chand Bansal, Anshul Gopal, Atulya Kumar Nagar: "Stability Analysis of Artificial Bee Colony Optimization Algorithm", Swarm and Evolutionary Computation, Volume 41, Pages 9-19, 2018 | Impact Factor: 7.177

https://doi.org/10.1016/j.swevo.2018.01.003

31

Jagdish Chand Bansal, Susheel Kumar Joshi, Atulya Kumar Nagar: "Fitness Varying Gravitational Constant in GSA", Applied Intelligence, 48, pages3446–3461(2018) | Impact Factor: 5.086

https://doi.org/10.1007/s10489-018-1148-8

30

Jagdish Chand Bansal, Susheel Kumar Joshi, Harish Sharma: "Modified global best artificial bee colony for constrained optimization problems", Computers & Electrical Engineering, Volume 67, Pages 365-382, 2018 | Impact Factor: 3.818

https://doi.org/10.1016/j.compeleceng.2017.10.021

29

Shimpi Singh Jadon, Ritu Tiwari, Harish Sharma and Jagdish Chand Bansal: "Hybrid Artificial Bee Colony Algorithm with Differential Evolution", Applied Soft Computing, Volume 58, Pages 11-24, 2017 | Impact Factor: 6.725

https://doi.org/10.1016/j.asoc.2017.04.018

28

Jagdish Chand Bansal and Pushpa Farswan: "Wind farm layout using Biogeography Based Optimization", Renewable Energy (2017), Volume 107, Pages 386-402  | Impact Factor: 6.274

https://doi.org/10.1016/j.renene.2017.01.064

27

Kavita Sharma, Kusum Deep, Jagdish Chand Bansal, Improving the local search ability of Spider Monkey Optimization algorithm using Quadratic Approximation for unconstrained optimization, Computational Intelligence (2017), Volume33, Issue2, 2017, Pages 210-240 | Impact Factor: 2.330

https://doi.org/10.1111/coin.12081 

26

Jagdish Chand Bansal, Pushpa Farswan, A novel Disruption in Biogeography-Based Optimization with application to optimal power flow problem, Applied Intelligence, 46, pages 590–615(2017) 46: 590 | Impact Factor: 5.086

https://doi.org/10.1007/s10489-016-0848-1

25

Ajay Sharma · Harish Sharma · Annapurna Bhargava · Nirmala Sharma · Jagdish Chand Bansal: "Optimal placement and sizing of capacitor using Limacon inspired Spider Monkey Optimization Algorithm", Memetic Computing, 9, pages311–331(2017) | Impact Factor: 5.9 

https://doi.org/10.1007/s12293-016-0208-z 

24

Shimpi Singh Jadon, Jagdish Chand Bansal and Ritu Tiwari: "Escalated convergent Artificial Bee Colony", Journal of Experimental & Theoretical Artificial Intelligence 28.1-2 (2016): 181-200. | Impact Factor: 2.340

https://doi.org/10.1080/0952813X.2015.1020523

23

Jagdish Chand Bansal, Harish Sharma, K V Arya, Kusum Deep, Millie Pant: "Self-adaptive artificial bee colony Optimization", Optimization, Volume 63, Issue 10, 1513--1532 (2014) Taylor & Francis. | Impact Factor: 2.360

https://doi.org/10.1080/02331934.2014.917302

22

Jagdish Chand Bansal, Harish Sharma, Shimpi Singh Jadon, Maurice Clerc: "Spider Monkey Optimization algorithm for numerical optimization". Memetic Computing 6(1): 31-47 (2014). | Impact Factor: 5.9

https://doi.org/10.1007/s12293-013-0128-0

21

Jagdish Chand Bansal, Harish Sharma, Shimpi Singh Jadon: "Artificial bee colony algorithm: a survey". International Journal of Advanced Intelligence Paradigms 5(1/2): 123-159 (2013). 

10.1504/IJAIP.2013.054681

20

Jagdish Chand Bansal, Harish Sharma, Atulya Nagar, K. V. Arya: "Balanced artificial bee colony algorithm". International Journal of Artificial Intelligence and Soft Computing 3(3): 222-243 (2013).

10.1504/IJAISC.2013.053392

19

Harish Sharma, Jagdish Chand Bansal, K. V. Arya: "Opposition based lévy flight artificial bee colony". Memetic Computing 5(3): 213-227 (2013). | Impact Factor: 5.9

https://doi.org/10.1007/s12293-012-0104-0

18

Jagdish Chand Bansal, Harish Sharma, K. V. Arya, Atulya Nagar: "Memetic search in artificial bee colony algorithm". Soft Computing. 17(10): 1911-1928 (2013). | Impact Factor: 3.643

https://doi.org/10.1007/s00500-013-1032-8

17

Harish Sharma, Jagdish Chand Bansal, K. V. Arya: "Power law-based local search in differential evolution", International Journal of Computational Intelligence Studies, Inderscience Publishers, 2(2), 90-112 (2013). 

https://doi.org/10.1080/00207721.2016.1165895

16

Jagdish Chand Bansal, Kusum Deep: "A Modified Binary Particle Swarm Optimization for Knapsack Problems". Applied Mathematics and Computation 218(22): 11042-11061 (2012). | Impact Factor: 4.091

https://doi.org/10.1016/j.amc.2012.05.001

15

Harish Sharma, Jagdish Chand Bansal, K. V. Arya, Kusum Deep: "Dynamic Swarm Artificial Bee Colony Algorithm". IJAEC 3(4): 19-33 (2012).

DOI: 10.4018/jaec.2012100102

14

Kusum Deep, Jagdish Chand Bansal: "Solving Economic Dispatch Problems with Valve-point Effects using Particle Swarm Optimization". J. UCS 18(13): 1842-1852 (2012). | Impact Factor: 1.139

13

Jagdish Chand Bansal, Harish Sharma: "Cognitive learning in differential evolution and its application to model order reduction problem for single-input single-output systems". Memetic Computing 4(3): 209-229 (2012). Impact Factor: 5.9

12

Harish Sharma, Jagdish Chand Bansal, K. V. Arya: "Fitness based Differential Evolution". Memetic Computing 4(4): 303-316 (2012). Impact Factor: 5.9

11

Harish Sharma, Jagdish Chand Bansal, K. V. Arya: "Self balanced differential evolution", Journal of Computational Science, Elsevier, (2012). | Impact Factor: 2.644

10

Kusum Deep, Jagdish Chand Bansal: "Hybridization of Particle Swarm Optimization with Quadratic Approximation", OPSEARCH (Springer Publication), 46(1): 3-24 (2009)

9

Kusum Deep, Jagdish Chand Bansal: "Mean Particle Swarm Optimization for Function Optimization", Int. J. Computational Intelligence Studies, 1(1): 72–92 (2009)

8

Harish Sharma, Jagdish Chand Bansal, K. V. Arya: "Power Law based Local Search in Artificial Bee Colony", International Journal of Artificial Intelligence and Soft Computing, Inderscience, 4(2): 164-194, 2014.

7

Harish Sharma, Jagdish Chand Bansal, K.V. Arya, Xin-She Yang: "Levy Flight Artificial Bee Colony", International Journal of system sciences, Taylor and Francis 2016 Impact Factor 2.285

6

Madhuri Arya, Kusum Deep, Jagdish Chand Bansal: "A Nature Inspired Adaptive Inertia weight in Particle Swarm Optimization", International Journal of Artificial Intelligence and Soft Computing, Inderscience., 2014

5

Shimpi Singh Jadon, Jagdish Chand Bansal, Ritu Tiwari, Harish Sharma: "Artificial bee colony algorithm with global and local neighborhoods", International Journal of Systems Assurance Engineering and Management, (2014). https://doi.org/10.1007/s13198-014-0286-6

4

Jagdish Chand Bansal, Shimpi Singh Jadon, Ritu Tiwari, Deep Kiran, B K Panigrahi: "Optimal Power flow Analysis using Artificial Bee Colony Algorithm with Global and Local Neighborhoods", International Journal of Systems Assurance Engineering and Management, (2017) 8(Suppl 4): 2158. https://doi.org/10.1007/s13198-014-0321-7

3

Shimpi Singh Jadon, Jagdish Chand Bansal, Ritu Tiwari, Harish Sharma: "Accelerating Artificial Bee Colony Algorithm with Adaptive Local Search", Memetic Computing, (2015) 7: 215. https://doi.org/10.1007/s12293-015-0158-x | Imapct Factor: 5.9

2

Kavita Sharma, Varsha Chhamunya, P C Gupta, Harish Sharma, Jagdish Chand Bansal, Fitness based particle swarm optimization, International Journal of System Assurance Engineering and Management, 2015, Volume 6, Issue 3, pp 319-329

1

Ajay Sharma, Harish Sharma, Annapurna Bhargava, Nirmala Sharma, Jagdish Chand Bansal, Optimal Power Flow Analysis using Levy Flight Spider Monkey Optimization Algorithm" International Journal of Artificial Intelligence and Soft Computing (IJAISC), Jan 2016, Vol. 5, Issue 4, pp. 320-352

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