My research focuses on developing hybrid computational frameworks that integrate artificial neural networks with advanced evolutionary and swarm-based optimization techniques for intelligent forecasting and complex problem-solving. I work on swarm intelligence approaches, particularly algorithms such as Influencer–Buddy Optimization and Follow-the-Leader, for multi-objective and constrained optimization. I have designed novel algorithms and adaptive mutation strategies to enhance accuracy, stability, and convergence, especially in short-term load and electricity price forecasting. These methodologies have also been extended to structural optimization and data classification problems. By incorporating multi-objective optimization, adaptive mechanisms, and nature-inspired search dynamics, my work addresses real-world challenges in energy systems and engineering design, delivering robust, scalable, and high-precision solutions across interdisciplinary domains.
Priyanka Singh, and Pragya Dwivedi. "Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem." Applied Energy (2018), 217: 537-549.
Priyanka Singh, Pragya Dwivedi, and Vibhor Kant. "A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting." Energy (2019), 174: 460-477
Priyanka Singh, and Pragya Dwivedi. “A Novel Hybrid Model Based on Neural Network and Multi-Objective Optimization for Effective Load Forecast.” Energy (2019), 182: 606-622.
Priyanka Singh and Rahul Kottath, “A Comparative Analysis of Hybrid Optimization Algorithms for Solving Constrained Engineering Problems.” Computers & Industrial Engineering (2021), 162: 107739.
Priyanka Singh, Rahul Kottath and G. Tejani, “Ameliorated Follow The Leader: Algorithm and Application to Truss Design Problem” Structure (2022), Vol. 42, pp. 181-204.
Priyanka Singh, and Rahul Kottath, "Chaos follow the leader algorithm: Application to data classification." Journal of Computational Science 65 (2022): 101886.
Priyanka Singh, and Rahul Kottath, “Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices." Utilities Policy 79 (2022): 101444.
Rahul Kottath, and Priyanka Singh, “Improved Follow the Leader (iFTL): a Swarm-Based Approach and its Application to Short-Term Electricity Price Forecasting”, Energy 263 (2023): 125641.
Rahul Kottath, Priyanka Singh, and Anirban Bhowmick. "Swarm-based hybrid optimization algorithms: an exhaustive analysis and its applications to electricity load and price forecasting." Soft Computing (2023): 1-32.
Priyanka Singh, and Rahul Kottath. "A step-size follow-the-leader optimization algorithm with an improved step parameters." Decision Analytics Journal 9 (2023): 100360.
Priyanka Singh, K.K. Mishra and Pragya Dwivedi, “Enhanced hybrid model for electricity load forecast through artificial neural network and Jaya algorithm.” 115-120. 10.1109/ICCONS.2017.8250660
Priyanka Singh, and Pragya Dwivedi, "Short-Term Electricity Load Forecast Using Hybrid Model Based on Neural Network and Evolutionary Algorithm." Numerical Optimization in Engineering and Sciences. Springer, Singapore, 2020. 167-176.
Priyanka Singh and Rahul Kottath. "Application of Mutation Operators on Grey Wolf Optimizer." 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2021.
Priyanka Singh, and Pragya Dwivedi, “Very Short-Term Load Forecasting with Hybrid Deep Learning Neural Network in Delhi, India.” In Soft Computing: Theories and Applications: Proceedings of SoCTA 2021, pp. 125-134. Singapore: Springer Nature Singapore, 2022.
Rahul kottath, and Priyanka Singh, “A Meta-heuristic Learning Approach for Short-term Price forecasting.” In Soft Computing: Theories and Applications: Proceedings of SoCTA 2021, pp. 147-156. Singapore: Springer Nature Singapore, 2022.
Priyanka Singh and Rahul Kottath. "A novel influencer mutation strategy for nature-inspired optimization algorithms to solve electricity price forecasting problem." In Advances in Computers, vol. 135, pp. 179-209. Elsevier, 2024.
Jahnvi Tiwari, Praveen Raj, Anushka Garg, Mahendra Pratap Yadav, Priyanka Singh, and Dheeraj Dubey. "A MERN Stack Framework for Real Estate Platforms Leveraging Conversational AI and Predictive Analytics." In 2025 IEEE 1st International Conference on Recent Trends in Computing and Smart Mobility (RCSM), pp. 1-8. IEEE, 2025.
BOSCH: Prediction of Energy Consumption (Completed)
Role: Principal Coordinator
Duration: 1 Year
AICTE QIP-PG Programme, 2024, 2025 & 2026
Role: Co-coordinator & Expert lecturer·
Budget: 20 lakh + Sponsored Candidate
Area: Machine Learning·
Courses taught: Hybrid Computing (4 Credits) + Foundations of ML & DL (4 Credits) + Project (4 Credits)
Institution Level Innovation Council (IIC) member in New Age Innovation Network (NAIN) 2.0 an initiative from Karnataka Govt.