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Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
Heuristics01:21

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Distributed Loads01:19

Distributed Loads

Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...

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Related Experiment Videos

Metaheuristic based scheduling meta-tasks in distributed heterogeneous computing systems.

Hesam Izakian1, Ajith Abraham, Václav Snášel

  • 1Islamic Azad University, Ramsar Branch, Ramsar, Iran;

Sensors (Basel, Switzerland)
|February 21, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces the Particle Swarm Optimization (PSO) algorithm for optimizing task scheduling in distributed computing systems. The PSO approach efficiently minimizes task completion time, outperforming existing methods.

Keywords:
distributed heterogeneous computing systemsparticle swarm optimizationscheduling

Related Experiment Videos

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Task scheduling in distributed heterogeneous computing systems is crucial for leveraging computational power.
  • The problem is computationally complex, classified as NP-complete.
  • Existing metaheuristic approaches like Genetic Algorithms (GA) have been applied.

Purpose of the Study:

  • To introduce and evaluate the Particle Swarm Optimization (PSO) algorithm for distributed heterogeneous computing system scheduling.
  • To minimize the makespan, defined as the completion time of the latest task.
  • To demonstrate the efficiency and superiority of the proposed PSO method.

Main Methods:

  • The study employs the Particle Swarm Optimization (PSO) algorithm, a population-based metaheuristic inspired by social behavior.
  • PSO simulates particles searching an solution space for optimal or near-optimal scheduling solutions.
  • The objective function is to minimize the makespan of task execution.

Main Results:

  • Experimental results indicate that the proposed PSO algorithm is highly efficient for task scheduling.
  • The PSO method demonstrates superior performance compared to previously reported PSO and GA approaches.
  • The algorithm effectively minimizes the makespan in distributed heterogeneous computing environments.

Conclusions:

  • The Particle Swarm Optimization (PSO) algorithm presents an effective metaheuristic solution for the NP-complete scheduling problem.
  • The proposed method offers significant improvements in efficiency and performance over existing techniques.
  • PSO is a viable and powerful tool for optimizing task scheduling in complex computing systems.