Dr. SHUBHAM GUPTA
Assistant Professor
Department of Mathematics
Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India-211004
E-mail: shubham.gupta[at]mnnit[dot]ac[dot]in
Telephone: +91 9520458076(O) +91 9520458076(M)
Research Publications
Journals:
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. (SCI, IF: 2.2)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, IF: 2.8)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. (SCI, IF: 2.2)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, IF: 2.9)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, IF: 8.8)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. (SCI, IF: 8.5) 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. (SCI, IF: 10.0) The most cited article from the journal 2018-2022.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. (SCI, IF: 8.7)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. (SCI, IF: 8.0)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. (SCI, IF: 8.7) 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. (SCI, IF: 5.3)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. (SCI, IF: 6.0)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. (SCI, IF: 8.5)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. (SCI, IF: 8.7)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. (SCI, IF: 8.5)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, IF: 2.9)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. (SCI, IF: 8.5)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. (SCI, IF: 8.5) 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. (SCI, IF: 8.7)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. (SCI, IF: 8.7)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. (SCI, IF: 5.570) Abidhan Bardhan, Anasua GuhaRay, Shubham Gupta, Biswajeet Pradhan, and Candan Gokceoglu (2021). A novel integrated approach of ELM and modified equilibrium optimizer for predicting soil compression index of subgrade layer of Dedicated Freight Corridor. Transportation Geotechnics, Vol 32, 1-21. DOI: https://doi.org/10.1016/j.trgeo.2021.100678. (SCI, IF: 4.938) 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. (SCI, IF: 8.7)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. (SCI, IF: 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. (SCI, IF: 8.5)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, IF: 8.8)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. (SCI, IF: 8.7)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, IF: 8.8)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, IF: 11.8)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, IF: 8.8).
Conference Proceedings:
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. 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.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.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.