Performance of Simulated Annealing, Tabu Search, and Evolutionary Algorithms forMulti-objective Network Partitioning

Authors

  • C. Gil Dept. Arquitectura de Computadores y Electrónica, Universidad de Almería, La Cañada de San Urbano s/n, 04120 Almería (Spain)
  • R. Baños Dept. Arquitectura de Computadores y Electrónica, Universidad de Almería, La Cañada de San Urbano s/n, 04120 Almería (Spain)
  • M. G. Montoya Dept. Arquitectura de Computadores y Electrónica, Universidad de Almería, La Cañada de San Urbano s/n, 04120 Almería (Spain)
  • J. Gómez Dept. Lenguajes y Computación, Universidad de Almería, La Cañada de San Urbano s/n, 04120 Almería (Spain)

Keywords:

Multi-objective Meta-heuristics, Simulated Annealing, Tabu Search, Evolutionary Computation, Network Partitioning

Abstract

Most real optimization problems often involve multiple objectives to optimize. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined but rather a set of solutions, so called Pareto-optimal set. Thus, the goal of multi-objective strategies is to obtain an approximation to this set. However, the majority of this kind of problem cannot be solved exactly as they have very large and highly complex search spaces. In recent years, meta-heuristics have become important tools for solving multi-objective problems encountered in industry as well as in the theoretical field. Thus far, there exist many comparative studies about the performance of evolutionary algorithms, but are few the papers dealing with non-evolutionary strategies. The goal of this paper is to analyze the performance of both paradigms in a realistic problem. In concrete, we have adapted five multi-objective meta-heuristics, based on Simulated Annealing, Tabu Search, and Evolutionary Methods, to solve the Network Partitioning Problem.

Downloads

Published

2006-01-24

How to Cite

Gil, C., Baños, R., Montoya, M. G., & Gómez, J. (2006). Performance of Simulated Annealing, Tabu Search, and Evolutionary Algorithms forMulti-objective Network Partitioning. Algorithmic Operations Research, 1(1). Retrieved from https://journals.lib.unb.ca/index.php/AOR/article/view/98

Issue

Vol. 1 No. 1 (2006)

Section

Articles

License

The copyright of the submitted article is hereby transferred to “Preeminent Academic Facets Inc. (the “publisher”) to the extent possible by the laws in the respective countries of the author(s). The author(s) retain all other rights including the following: 1. Any patents and trademark rights. 2. Right to post a pre-print version of the paper on the author’s personal website which is not used for commercial purposes. The author(s) warrant that the work is original, it has not been published elsewhere, it is not currently under consideration for publication in any form, and it does not infringe any copyright laws. The signing author or agent further warrants that all authors, if applicable, have been informed of the terms of this agreement and obtained the approval and authority to sign the agreement.