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Migration study on a pareto-based island model for MOACOs

Published: 06 July 2013 Publication History

Abstract

Pareto-based island model is a multi-colony distribution scheme recently presented for the resolution, by means of ant colony optimization algorithms, of bi-criteria problems. It yielded very promising results, but the model was implemented considering a unique Pareto-front-shaped unidirectional neighborhood migration topology, and a constant migration rate. In the present work two additional neighborhood topology schemes, and four different migration rates have been tested, considering the algorithm which obtained the best results in average in the model presentation article: MOACS (Multi-Objective Ant Colony System). Several experiments have been conducted, including statistical tests for better support the study. High values for the migration rate and the use of a bidirectional neighborhood migration topology yields the best results.

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  • (2016)Solving the Traveling Salesman's Problem using the African Buffalo OptimizationComputational Intelligence and Neuroscience10.1155/2016/15102562016(3-3)Online publication date: 1-Jan-2016

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cover image ACM Conferences
GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation
July 2013
1672 pages
ISBN:9781450319638
DOI:10.1145/2463372
  • Editor:
  • Christian Blum,
  • General Chair:
  • Enrique Alba
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 July 2013

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Author Tags

  1. aco
  2. distributed algorithms
  3. island model
  4. migration rate
  5. moaco
  6. multi-colony
  7. multi-objective ant colony optimization algorithms
  8. neighborhood migration topology

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GECCO '13
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GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2016)Solving the Traveling Salesman's Problem using the African Buffalo OptimizationComputational Intelligence and Neuroscience10.1155/2016/15102562016(3-3)Online publication date: 1-Jan-2016

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