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ACOhg: dealing with huge graphs

Published: 07 July 2007 Publication History

Abstract

Ant Colony Optimization (ACO) has been successfully applied to those combinatorial optimization problems which can be translated into a graph exploration. Artificial ants build solutions step by step adding solution components that are represented by graph nodes. The existing ACO algorithms are suitable when the graph is not very large (thousands of nodes) but is not useful when the graph size can be a challenge for the computer memory and cannot be completely generated or stored in it. In this paper we study a new ACO model that overcomes the difficulties found when working with a huge construction graph. In addition to the description of the model, we analyze in the experimental section one technique used for dealing with this huge graph exploration. The results of the analysis can help to understand the meaning of the new parameters introduced and to decide which parameterization is more suitable for a given problem. For the experiments we use one real problem with capital importance in Software Engineering: refutation of safety properties in concurrent systems. This way, we foster an innovative research line related to the application of ACO to formal methods in Software Engineering.

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  • (2024)Memoization in Model Checking for Safety Properties with Multi-Swarm Particle Swarm OptimizationElectronics10.3390/electronics1321419913:21(4199)Online publication date: 25-Oct-2024
  • (2022)Unleashing the power of compiler intermediate representation to enhance neural program embeddingsProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510217(2253-2265)Online publication date: 21-May-2022
  • (2022)Exploration strategies for balancing efficiency and comprehensibility in model checking with ant colony optimizationJournal of Information and Telecommunication10.1080/24751839.2022.20474706:3(341-359)Online publication date: 22-Mar-2022
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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
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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Publication History

Published: 07 July 2007

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Author Tags

  1. SPIN
  2. ant colony optimization
  3. metaheuristics

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2024)Memoization in Model Checking for Safety Properties with Multi-Swarm Particle Swarm OptimizationElectronics10.3390/electronics1321419913:21(4199)Online publication date: 25-Oct-2024
  • (2022)Unleashing the power of compiler intermediate representation to enhance neural program embeddingsProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510217(2253-2265)Online publication date: 21-May-2022
  • (2022)Exploration strategies for balancing efficiency and comprehensibility in model checking with ant colony optimizationJournal of Information and Telecommunication10.1080/24751839.2022.20474706:3(341-359)Online publication date: 22-Mar-2022
  • (2021)Exploration Strategies for Model Checking with Ant Colony OptimizationComputational Collective Intelligence10.1007/978-3-030-88081-1_20(264-276)Online publication date: 30-Sep-2021
  • (2020)A Survey on the Applications of Swarm Intelligence to Software VerificationHandbook of Research on Fireworks Algorithms and Swarm Intelligence10.4018/978-1-7998-1659-1.ch017(376-398)Online publication date: 2020
  • (2019)Scaling techniques for parallel ant colony optimization on large problem instancesProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321832(47-54)Online publication date: 13-Jul-2019
  • (2019)MS-ACOSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3444-y23:12(4531-4556)Online publication date: 1-Jun-2019
  • (2018)Solving Order/Degree Problems by Using EDA-GK with a Novel Sampling MethodJournal of Advanced Computational Intelligence and Intelligent Informatics10.20965/jaciii.2018.p023622:2(236-241)Online publication date: 20-Mar-2018
  • (2017)Effective heuristics for ant colony optimization to handle large-scale problemsSwarm and Evolutionary Computation10.1016/j.swevo.2016.06.00632(140-149)Online publication date: Feb-2017
  • (2017)ACO Based Model Checking Extended by Smell-Like Pheromone with Hop CountsHarmony Search Algorithm10.1007/978-981-10-3728-3_7(52-63)Online publication date: 29-Jan-2017
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