Shaya Sheikh

Multi-objective energy aware multiprocessor scheduling using bat intelligence

In this paper, a new heuristic called bat intelligence (BI) is introduced for solving energy aware multiprocessor scheduling problems. Bat intelligence is a novel optimization method that models prey hunting behaviors of bats. Bat intelligence and genetic algorithm (GA) are used to solve single-objective multiprocessor scheduling problem using, makespan, tardiness, and energy consumption as objective functions. Bat intelligence shows considerable improvement in terms of solution quality when compared with GA. Different combinations of these objectives are used to solve bi-objective multiprocessor scheduling problems, (makespan vs. energy, and also tardiness vs. energy). Tri-objective multiprocessor scheduling problem is also presented at the end. To generate desirable efficient alternatives, a Normalized Weighted Additive Utility Function is used. Simulation shows that BI identifies a set of efficient solutions that correspond to the assigned weights. The computational simulation also shows conflicting relationships between makespan and energy, and also between tardiness and energy.

Mobile ad hoc network proactive routing with delay prediction using neural network

Existing MANET routing protocols rely heavily on hop count evaluation. Although this is simple and efficient, it sacrifices the potential performance gains obtainable by considering other dynamic routing metrics. In this paper, we propose a delay prediction mechanism and its integration with a MANET proactive routing protocol. We demonstrate our approach of predicting mean queuing delay as a nonstationary time series using appropriate neural network models: Multi-Layer Perceptron or Radial Basis Function. To support MANET proactive routing, our delay prediction mechanism is devised as a distributed, independent, and continuous neural network training and prediction process conducted on individual nodes. We integrated our delay prediction mechanism with a well-known MANET proactive routing protocol—OLSR. The essential part of this integration is our TierUp algorithm, which is a novel node-state routing table computation algorithm. The structure and the key parameters of the resulting extended OLSR, called OLSR_NN, are also discussed. Our simulation shows that because of its capability of balancing the traffic, OLSR_NN is able to increase data packet delivery ratio and reduce average end-to-end delay in scenarios with complex traffic patterns and wide range of node mobility, compared to OLSR.

Integrated risk and multi-objective optimization of energy systems

Developing risk aware energy planning model considering storages as energy traders.Providing optimal risk strategy for each storage.Robust, simple, and verifiable framework for understanding and measuring risk scenarios for energy systems. This paper presents a risk aware energy planning model with storages as decision makers. The amount of energy bought from generators, stored in storages, and sold to users is set by storages. The paper also provides the optimal risk strategy for each storage considering energy cost, environmental impact, and failure rate as social welfare functions. A robust, simple, and verifiable framework for understanding and measuring risk scenarios for energy systems is introduced. Multiple-objective approach for integrating risk with social welfare functions is also discussed. Application of presented framework in energy industry is discussed by examples.