Journal Publications (SCI/SCIE)

1. Shubham Gupta, & Rong Su (2023). “Diversity-enhanced modified sine cosine algorithm and its application in solving engineering design problems”, Journal of Computational Science, 72. DOI: https://doi.org/10.1016/j.jocs.2023.102105. (SCI, Q1, IF: 3.3). 

2.  Shubham Gupta, Shu Weihua, Yi Zhang & Rong Su (2023). “Differential evolution-driven traffic light scheduling for vehicle-pedestrian mixed-flow networks”, Knowledge-Based Systems, 274. DOI: https://doi.org/10.1016/j.knosys.2023.110636. (SCI, Q1, IF: 8.8). 

3. Shubham Gupta, Shitu Singh, Rong Su, Shangce Gao, & Jagdish Chand Bansal (2023). “Multiple Elite Individual Guided Piecewise Search-Based Differential Evolution”. IEEE/CAA Journal of Automatica Sinica, 10. (SCI, Q1, IF: 11.8)

4. Shubham Gupta, & Rong Su (2022). “Multiple individual guided differential evolution with time varying and feedback information-based control parameters”. Knowledge-Based Systems, 259. DOI: https://doi.org/10.1016/j.knosys.2022.110091. (SCI, Q1, IF: 8.8)

5.  Shubham Gupta, Rong Su, & Shitu Singh (2022). “Diversified sine cosine algorithm based on differential evolution for multidimensional knapsack problem”. Applied Soft Computing, 130. DOI: https://doi.org/10.1016/j.asoc.2022.109682. (SCIE, Q1, IF: 8.7)

6. Shubham Gupta, & Rong Su (2022). “An efficient differential evolution with fitness-based dynamic mutation strategy and control parameters”. Knowledge-Based Systems, 251. DOI: https://doi.org/10.1016/j.knosys.2022.109280. (SCI, Q1, IF: 8.8)

7. Shubham Gupta (2022). “Enhanced sine cosine algorithm with crossover: A comparative study and empirical analysis”. Expert Systems with Applications, 198. DOI: https://doi.org/10.1016/j.eswa.2022.116856. (SCIE, Q1, IF: 8.5)

8.  Shubham Gupta, Yi Zhang, & Rong Su (2022). “Urban traffic light scheduling for pedestrian-vehicle mixedflow networks using discrete sine cosine algorithm and its variants”. Applied Soft Computing, 120. DOI: https://doi.org/10.1016/j.asoc.2022.108656. (SCIE, Q1, IF: 8.7)

9.  Shubham Gupta (2021). “Enhanced harmony search algorithm with non-linear control parameters for global optimization and engineering design problems”. Engineering with Computers, 1-24. DOI: https://doi.org/10.1007/s00366-021-01467-8. (SCIE, Q1, IF: 8.7)

10.  Navid Kardani, Abidhan Bardhan, Shubham Gupta, Pijush Samui, Majidreza Nazem, Yanmei Zhang, Annan Zhou (2021). Predicting permeability of tight carbonates using a hybrid machine learning approach of modified equilibrium optimizer and extreme learning machine. Acta Geotechnica, 1-17. DOI: https://doi.org/10.1007/s114 40-021-01257-y. (SCIE, Q1, IF: 5.7) 

11.  Shubham Gupta, Kusum Deep, Ali Asghar Heidari, Hossein Moayedi, & Huiling Chen (2021). “Harmonized salp chain-built optimization”. Engineering with Computers, 37(2), 1049-1079. DOI: https://doi.org/10.1007/s00366- 019-00871-5. (SCIE, Q1, IF: 8.7)

12.  Shubham Gupta, Kusum Deep,  Hossein Moayedi, Loke Kok Foong, & Assif Assad (2021). “Sine cosine grey wolf optimizer to solve engineering design problems”. Engineering with Computers, 37(4), 3123-3149. DOI: https://doi.org/10.1007/s00366-020-00996-y. (SCIE, Q1, IF: 8.7)

13.  Shubham Gupta, Hammoudi Abderazek, Betül Sultan Yıldız, Ali Riza Yildiz, Seyedali Mirjalili, & Sadiq M. Sait (2021). “Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems”. Expert Systems with Applications, 183. DOI: https://doi.org/10.1016/j.eswa.2021.115351. (SCIE, Q1, IF: 8.5) 

14.  Shubham Gupta, Kusum Deep,  Ali Asghar Heidari, Hossein Moayedi, & Mingjing Wang (2020). “Opposition-based learning Harris hawks optimization with advanced transition rules: Principles and analysis”. Expert Systems with Applications, 158. DOI: https://doi.org/10.1016/j.eswa.2020.113510. (SCIE, Q1, IF: 8.5)

15.  Shubham Gupta, & Kusum Deep. (2020). “Optimal coordination of overcurrent relays using improved leadershipbased grey wolf optimizer”. Arabian Journal for Science and Engineering, 45(3), 2081-2091. DOI: https://doi.org/10.1007/s13369-019-04025-z. (SCI, Q2, IF: 2.9)

16.  Shubham Gupta, Kusum Deep, Seyedali Mirjalili, & Joong Hoon Kim (2020). “A modified sine cosine algorithm with novel transition parameter and mutation operator for global optimization”. Expert Systems with Applications, 154. DOI: https://doi.org/10.1016/j.eswa.2020.113395. (SCIE, Q1, IF: 8.5)

17.  Shubham Gupta, & Kusum Deep. (2020). “A memory-based grey wolf optimizer for global optimization tasks”. Applied Soft Computing, 93. DOI:https://doi.org/10.1016/j.asoc.2020.106367. (SCIE, Q1, IF: 8.7)

18.  Zhenyu Lei, Shangce Gao, Shubham Gupta, Jiujun Cheng, & Gang Yang (2020). “An aggregative learning gravitational search algorithm with self-adaptive gravitational constants”. Expert Systems with Applications, 152. DOI: https://doi.org/10.1016/j.eswa.2020.113396. (SCIE, Q1, IF: 8.5)

19.  Shubham Gupta, & Kusum Deep. (2020). “Hybrid sine cosine artificial bee colony algorithm for global optimization and image segmentation”. Neural Computing and Applications, 32(13), 9521-9543. DOI: https://doi.org/10.1007/s00521-019-04465-6. (SCIE, Q1, IF: 6.0)

20.  Shubham Gupta, & Kusum Deep. (2020). “A novel hybrid sine cosine algorithm for global optimization and its application to train multilayer perceptrons”. Applied Intelligence, 50(4), 993-1026. DOI: https://doi.org/10.1007/s10489-019-01570-w. (SCIE, Q2, IF: 5.3)

21.  Shubham Gupta, Kusum Deep, & Seyedali Mirjalili (2020). “An efficient equilibrium optimizer with mutation strategy for numerical optimization”. Applied Soft Computing, 96. DOI: https://doi.org/10.1016/j.asoc.20 20.106542. (SCIE, Q1, IF: 8.7) 

22.  Shubham Gupta, Kusum Deep, & Engelbrecht, A. P. (2020). “A memory guided sine cosine algorithm for global optimization”. Engineering Applications of Artificial Intelligence, 93. DOI: https://doi.org/10.1016/j. engappai.2020.103718. (SCIE, Q1, IF: 8.0)

23.  Shubham Gupta, & Kusum Deep (2020). “Enhanced leadership-inspired grey wolf optimizer for global optimization problems”. Engineering with Computers, 36(4), 1777-1800. DOI: https://doi.org/10.1007/s0 0366-019-00795-0. (SCIE, Q1, IF: 8.7)

24.  Shubham Gupta, & Kusum Deep (2019). “A novel random walk grey wolf optimizer”. Swarm and evolutionary computation, 44, 101-112. DOI: https://doi.org/10.1016/j.swevo.2018.01.001. (SCIE, Q1, IF: 10.0)  The most cited article from the journal 2018-2022.

25.  Shubham Gupta, & Kusum Deep (2019). “A hybrid self-adaptive sine cosine algorithm with opposition based learning”. Expert Systems with Applications, 119, 210-230. DOI: https://doi.org/10.1016/j.eswa.2018. 10.050. (SCIE, Q1, IF: 8.5) 

26.  Shubham Gupta, & Kusum Deep (2019). “Improved sine cosine algorithm with crossover scheme for global optimization”. Knowledge-Based Systems, 165, 374-406. DOI: https://doi.org/10.1016/j.knosys.2018.12.0 08. (SCI, Q1, IF: 8.8)

27.  Shubham Gupta, & Kusum Deep (2019). “An efficient grey wolf optimizer with opposition-based learning and chaotic local search for integer and mixed-integer optimization problems”. Arabian Journal for Science and Engineering, 44(8), 7277-7296. DOI: https://doi.org/10.1007/s13369-019-03806-w. (SCI, Q2, IF: 2.9)

28.  Shubham Gupta, & Kusum Deep (2019). “An opposition-based chaotic grey wolf optimizer for global optimisation tasks”. Journal of Experimental & Theoretical Artificial Intelligence, 31(5), 751-779. DOI: https://doi.org/10.1080/0952813X.2018.1554712.  (SCIE, Q2, IF: 2.2)

29.  Shubham Gupta, & Kusum Deep (2018). “Random walk grey wolf optimizer for constrained engineering optimization problems”. Computational Intelligence, 34(4), 1025-1045. DOI: https://doi.org/10.1111/coi n.12160. (SCI, Q3, IF: 2.8)

30.  Shubham Gupta, & Kusum Deep (2018). “Cauchy grey wolf optimiser for continuous optimisation problems”. Journal of Experimental & Theoretical Artificial Intelligence, 30(6), 1051-1075. DOI: https://doi.org /10.1080/0952813X.2018.1513080. (SCIE, Q2, IF: 2.2) 

Conference Papers

1.  Shubham Gupta, Kusum Deep, Assif Assad (2020). Reliability-redundancy allocation using random walk grey wolf optimizer. In Proceedings of 8th International Conference on Soft Computing for Problem Solving. (pp. 941-959). Springer.

2.  Shubham Gupta, Kusum Deep (2019). Improved Grey Wolf Optimizer Based on Opposition-Based Learning. In Proceedings of 7th International Conference on Soft Computing for Problem Solving. (pp. 327-338). Springer.

3.  Shubham Gupta, Kusum Deep (2019). Hybrid Grey Wolf Optimizer with mutation operator. In Proceedings of 7th International Conference on Soft Computing for Problem Solving. (pp. 961-968). Springer.

4.  Shubham Gupta, Kusum Deep (2017). Performance of grey wolf optimizer on large scale problems. In AIP conference proceedings, vol. 1802, no. 1, p. 020005. AIP Publishing.