Demand Side Management in Smart Grid Using Heuristic Optimization
- T. Logenthiran, D. Srinivasan, Tanaka Shun
- EngineeringIEEE Transactions on Smart Grid
- 8 June 2012
A heuristic-based Evolutionary Algorithm that easily adapts heuristics in the problem was developed for solving this minimization problem and results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.
Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals
- Hao Quan, D. Srinivasan, A. Khosravi
- EngineeringIEEE Transactions on Neural Networks and Learning…
- 1 February 2014
A neural network (NN)-based method for the construction of prediction intervals (PIs) and a new problem formulation is proposed, which translates the primary multiobjectives problem into a constrained single-objective problem.
An Introduction to Multi-Agent Systems
- P. Balaji, D. Srinivasan
- Computer Science
- 2010
The goal of this chapter is to provide a quick reference to assist in the design of multi-agent systems and to highlight the merit and demerits of the existing methods.
A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition
- Anupam Trivedi, D. Srinivasan, K. Sanyal, Abhiroop Ghosh
- Computer ScienceIEEE Transactions on Evolutionary Computation
- 1 June 2017
A comprehensive survey of the decomposition-based MOEAs proposed in the last decade is presented, including development of novel weight vector generation methods, use of new decomposition approaches, efficient allocation of computational resources, modifications in the reproduction operation, mating selection and replacement mechanism, hybridizing decompositions- and dominance-based approaches, etc.
Neural-network-based signature recognition for harmonic source identification
- D. Srinivasan, W. S. Ng, A. Liew
- Computer ScienceIEEE Transactions on Power Delivery
- 2006
A neural-network (NN)-based approach to nonintrusive harmonic source identification and MLP was found to be the best signature identification method because of its low computational requirements and ability to extract the information necessary for highly accurate device identification.
Multiobjective Evolutionary Algorithm With Controllable Focus on the Knees of the Pareto Front
- L. Rachmawati, D. Srinivasan
- Computer ScienceIEEE Transactions on Evolutionary Computation
- 1 August 2009
This paper presents a selection scheme that enables a multiobjective evolutionary algorithm (MOEA) to obtain a nondominated set with controllable concentration around existing knee regions of the Pareto front and demonstrates that convergence on the Paredto front is not compromised by imposing the preference-based bias.
A unified differential evolution algorithm for constrained optimization problems
- Anupam Trivedi, Krishnendu Sanyal, P. Verma, D. Srinivasan
- Computer ScienceIEEE Congress on Evolutionary Computation
- 1 June 2017
The experimental results demonstrate the efficacy of the presented algorithm in solving constrained real parameter optimization problems and inspired from some popular DE variants existing in the literature such as CoDE, JADE, SaDE, and ranking-based mutation.
Mobile agents based routing protocol for mobile ad hoc networks
- S. Marwaha, C. Tham, D. Srinivasan
- Computer ScienceGlobal Telecommunications Conference, . GLOBECOM…
- 17 November 2002
Through extensive simulations in this paper it is proved that the proposed Ant-AODV hybrid routing technique, is able to achieve reduced end-to-end delay compared to conventional ant-based and AODV routing protocols.
Neural Networks for Real-Time Traffic Signal Control
- D. Srinivasan, M. Choy, R. Cheu
- Computer ScienceIEEE transactions on intelligent transportation…
- 1 September 2006
Simulation results show that the hybridmultiagent system provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases.
Urban traffic signal control using reinforcement learning agents
- P. Balaji, X. German, D. Srinivasan
- Computer Science
- 30 August 2010
The proposed multi- agent reinforcement learning (RLA) signal control showed significant improvement in mean time delay and speed in comparison to other traffic control system like hierarchical multi-agent system (HMS), cooperative ensemble (CE) and actuated control.
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