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Mutation, Gene Flow, and Genetic Drift

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Related Experiment Videos

A memetic genetic algorithm for the vertex p-center problem.

Wayne Pullan1

  • 1School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia. w.pullan@griffith.edu.au

Evolutionary Computation
|September 25, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces PBS, a population-based meta-heuristic for the p-center problem. PBS effectively minimizes maximum client-facility costs and shows performance comparable to existing algorithms.

Related Experiment Videos

Area of Science:

  • Operations Research
  • Computer Science
  • Optimization

Background:

  • The p-center problem involves selecting 'p' facilities to minimize the maximum distance to any client.
  • This is a critical problem in facility location and network design.

Purpose of the Study:

  • To introduce and evaluate PBS, a novel population-based meta-heuristic for solving the p-center problem.
  • To assess PBS's performance against established exact and approximate algorithms.

Main Methods:

  • PBS utilizes a genetic algorithm framework with phenotype crossover and directed mutation.
  • It incorporates a local search component to refine solutions.
  • The algorithm is designed for efficient parallel processing on multiple computer processors.

Main Results:

  • PBS demonstrates effective minimization of the maximum client-facility assignment cost.
  • Empirical results show PBS achieves performance comparable to state-of-the-art algorithms.
  • The method scales well for larger p-center problem instances.

Conclusions:

  • PBS is a competitive and effective meta-heuristic for the p-center problem.
  • Its parallel processing capability makes it suitable for large-scale instances.
  • PBS offers a valuable alternative for optimizing facility location decisions.