Heuristic Approach for Scheduling Dependent Real-Time Tasks

Reducing energy consumption is a critical issue in the design of battery-powered real time systems to prolong battery life. With dynamic voltage scaling (DVS) processors, energy consumption can be reduced efficiently by making appropriate decisions on the processor speed/voltage during the scheduling of real time tasks. Scheduling decision is usually based on parameters which are assumed to be crisp. However, in many circumstances the values of these parameters are vague. The vagueness of parameters suggests that to develop a fuzzy logic approach to reduce energy consumption by determining the appropriate supply-voltage/speed of the processor provided that timing constraints are guaranteed. Intensive simulated experiments and qualitative comparisons with the most related literature have been conducted in the context of dependent real-time tasks. Experimental results have shown that the proposed fuzzy scheduler saves more energy and creates feasible schedules for real time tasks. It also considers tasks priorities which cause higher system utilization and lower deadline miss time

The mean field theory of spin glasses: the heuristic replica approach and recent rigorous results

The mathematically correct computation of the spin glasses free energy in the infinite range limit crowns 25 years of mathematic efforts in solving this model. The exact solution of the model was found many years ago by using a heuristic approach; the results coming from the heuristic approach were crucial in deriving the mathematical results. The mathematical tools used in the rigorous approach are quite different from those of the heuristic approach. In this note we will review the heuristic approach to spin glasses in the light of the rigorous results; we will also discuss some conjectures that may be useful to derive the solution of the model in an alternative way.Comment: 12 pages, 1 figure; lecture at the Flato Colloquia Day, Thursday 27 November, 200

Energy Aware Heuristic Approach for Cluster Head Selection in Wireless Sensor Networks

Wireless sensor networks idea is envisioned and defined as self-deployed, error prone, long living inexpensive communication devices that are densely deployed to collect data from physical space. Sensor nodes communicate with each other to detect events depending on the application, to collect and process data, and to transmit the sensed information to the base station by hopping the data from node to node. Major challenge in wireless network is energy consumption, since the sensor devices are deployed on unattended wide areas and replacing their batteries is not very feasible. Therefore, designing energy-aware algorithms becomes a major challenge for extending the lifetime of sensors. The lifetime of the network can be maximized by selecting the most optimal cluster head. In this paper, a heuristic decision making approach is proposed for producing energy-aware clusters with optimal selection of cluster head. This helps to improve the efficiency and accuracy and overcome the drawbacks like getting trapped at a local extreme in the optimization process

A heuristic approach for multiple restricted multiplication

Published versio

A Heuristic Approach to Portfolio Optimization

Constraints on downside risk, measured by shortfall probability, expected shortfall, semi-variance etc., lead to optimal asset allocations which differ from the meanvariance optimum. The resulting optimization problem can become quite complex as it exhibits multiple local extrema and discontinuities, in particular if we also introduce constraints restricting the trading variables to integers, constraints on the holding size of assets or on the maximum number of different assets in the portfolio. In such situations classical optimization methods fail to work efficiently and heuristic optimization techniques can be the only way out. The paper shows how a particular optimization heuristic, called threshold accepting, can be successfully used to solve complex portfolio choice problems.Portfolio Optimization; Downside Risk Measures;Heuristic Optimization Threshold Accepting.

Partitioning networks into cliques: a randomized heuristic approach

In the context of community detection in social networks, the term community can be grounded in the strict way that simply everybody should know each other within the community. We consider the corresponding community detection problem. We search for a partitioning of a network into the minimum number of non-overlapping cliques, such that the cliques cover all vertices. This problem is called the clique covering problem (CCP) and is one of the classical NP-hard problems. For CCP, we propose a randomized heuristic approach. To construct a high quality solution to CCP, we present an iterated greedy (IG) algorithm. IG can also be combined with a heuristic used to determine how far the algorithm is from the optimum in the worst case. Randomized local search (RLS) for maximum independent set was proposed to find such a bound. The experimental results of IG and the bounds obtained by RLS indicate that IG is a very suitable technique for solving CCP in real-world graphs. In addition, we summarize our basic rigorous results, which were developed for analysis of IG and understanding of its behavior on several relevant graph classes

A heuristic approach to the weakly interacting Bose gas

Some thermodynamic properties of weakly interacting Bose systems are derived from dimensional and heuristic arguments and thermodynamic relations, without resorting to statistical mechanics

Chemical Physics of Phonons & Superconductivity: A Heuristic Approach

Several lines of thought are pursued in an attempt to further clarify the role played by phonons in the theory of superconductivity. The central results of BCS theory are examined in the context of anharmonicity in the phonon motion and the role that Badger's rule of spectroscopy can play in simplifying and making more chemically intuitive the nature of the mechanism of superconductivity