research-article

Parameter Adaptation for Ant Colony System Algorithm using Hidden Markov Model for TSP Problems

Authors:

Safae Bouzbita,

Abdellatif El Afia,

Rdouan FaiziAuthors Info & Claims

LOPAL '18: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications

Article No.: 6, Pages 1 - 6

https://doi.org/10.1145/3230905.3230962

Published: 02 May 2018 Publication History

Abstract

In this paper we control the potentials of exploration and exploitation into the Ant Colony System (ACS) by dynamically adapting the parameter relative to the importance of heuristic information β and the parameter that determines the importance of exploitation versus exploration q0 parameters using the Hidden Markov Model (HMM). Two metrics have been proposed to measure the performance of the algorithm, which are considered as the hidden states of the proposed HMM. Those metrics allow the choice of the suitable value of β and q0 to make the best possible next move to construct a feasible solution in ACS. The adaptive ACS algorithm was tested on some TSP problems with different sizes and a comparison to the standard algorithm was undertaken.

References

[1]

Dorigo, M., Birattari, M., and Stutzle, T. 2006. Ant colony optimization. IEEE computational intelligence magazine, 1(4), 28--39.

Digital Library

[2]

Dorigo, M and Di Caro, G. 1999. Ant colony optimization: a new meta-heuristic. In Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on (Vol. 2, pp. 1470--1477). IEEE.

[3]

Bouzbita, S., El Afia, A., and Faizi, R. Hidden Markov Model classifier for the adaptive ACS-TSP pheromone parameter. In bioinspired Heuristics for Optimization (pp. 143--156) Springer, Cham.

[4]

Bouzbita, S., El Afia, A., Faizi, R., and Zbakh, M. 2016. Dynamic adaptation of the ACS-TSP local pheromone decay parameter based on the Hidden Markov Model. In Cloud Computing Technologies and Applications (CloudTech), 2016 2nd International Conference on (pp. 344--349). IEEE.

[5]

Bouzbita, S., El Afia, A., and Faizi, R. 2016. A novel based Hidden Markov Model approach for controlling the ACS-TSP evaporation parameter. In Multimedia Computing and Systems (ICMCS), 2016 5th International Conference on (pp. 633--638). IEEE.

[6]

El Afia, A., Bouzbita, S., and Faizi, R. 2017. The Effect of Updating the Local Pheromone on ACS Performance using Fuzzy Logic. International Journal of Electrical and Computer Engineering (IJECE), 7(4), 2161--2168.

[7]

El Afia, A., Lalaoui, M., and Chiheb, R. 2017. Fuzzy logic controller for an adaptive Huang cooling of simulated annealing. In Proceedings of the 2nd international Conference on Big Data, Cloud and Applications (p. 64). ACM.

Digital Library

[8]

El Afia, A., Sarhani, M., and Aoun, O. 2017. Hidden markov model control of inertia weight adaptation for Particle swarm optimization. IFAC-PapersOnLine, 50(1), 9997--10002.

[9]

El Afia, A., & Kabbaj, M. M. 2017. Supervised learning in Branch-and-cut strategies. In Proceedings of the 2nd international Conference on Big Data, Cloud and Applications (p. 114). ACM.

Digital Library

[10]

Aoun, O., Sarhani, M., and El Afia, A. 2016. Investigation of hidden markov model for the tuning of metaheuristics in airline scheduling problems. IFAC-PapersOnLine, 49(3), 347--352.

[11]

Aoun, O., Sarhani, M., and El Afia, A. 2018. Hidden markov model classifier for the adaptive particle swarm optimization. In Recent Developments in Metaheuristics (pp. 1--15). Springer, Cham.

[12]

Aoun, O., Sarhani, M., and El Afia, A. (in press). Particle swarm optimisation with population size and acceleration coefficients adaptation using hidden Markov model state classification. International Journal of Metaheuristics.

[13]

Lalaoui, M., El Afia, A. and Chiheb, R. 2016. Hidden Markov Model for a self-learning of Simulated Annealing cooling law. In Multimedia Computing and Systems (ICMCS), 2016 5th International Conference on (pp. 558--563).IEEE.

[14]

Lalaoui, M., El Afia, A., and Chiheb, R. 2018. A Self-Tuned Simulated Annealing Algorithm Using Hidden Markov Model. International Journal of Electrical and Computer Engineering (IJECE), 8(1), 291--298.

[15]

Lalaoui, M., El Afia, A., and Chiheb, R. 2017. A self-adaptive very fast simulated annealing based on Hidden Markov model. In Cloud Computing Technologies and Applications (CloudTech), 2017 3rd International Conference of (pp. 1--8). IEEE.

[16]

Kabbaj, M. M., and El Afia, A. 2016. Towards learning integral strategy of branch and bound. In Multimedia Computing and Systems (ICMCS), 2016 5th International Conference on (pp. 621--626). IEEE.

[17]

Pilat, M. L., and White, T. 2002. Using genetic algorithms to optimize ACS-TSP. In International Workshop on Ant Algorithms (pp. 282--287). Springer, Berlin, Heidelberg.

Digital Library

[18]

Randall, M. 2004. Near parameter free ant colony optimisation. In International Workshop on Ant Colony Optimization and Swarm Intelligence (pp. 374--381). Springer, Berlin, Heidelberg.

[19]

Anghinolfi, D., Boccalatte, A., Paolucci, M., and Vecchiola, C. 2008. Performance evaluation of an adaptive ant colony optimization applied to single machine scheduling. In Asia-Pacific Conference on Simulated Evolution and Learning (pp. 411--420). Springer, Berlin, Heidelberg.

Digital Library

[20]

Melo, L., Pereira, F., and Costa, E. 2009. MC-ANT: a multi-colony ant algorithm. In International Conference on Artificial Evolution (Evolution Artificielle) (pp. 25--36). Springer, Berlin, Heidelberg.

Digital Library

[21]

Hao, Z. F., Cai, R. C., and Huang, H. 2006. An adaptive parameter control strategy for ACO. In Machine Learning and Cybernetics, 2006 International Conference on (pp. 203--206). IEEE.

[22]

Amir, C., Badr, A., and Farag, I. 2007. A fuzzy logic controller for ant algorithms. Computing and Information Systems, 11(2), 26.

[23]

Gaertner, D., and Clark, K. L. 2005. On Optimal Parameters for Ant Colony Optimization Algorithms. In IC-AI (pp. 83--89).

[24]

Stützle, T., et al. 2011. Parameter adaptation in ant colony optimization. In Autonomous search (pp. 191--215). Springer, Berlin, Heidelberg

[25]

Reinelt, G. (1991). TSPLIB---A traveling salesman problem library. ORSA journal on computing, 3(4), 376--384.

Cited By

Boulaich MOhamouddou SMeliani HEnnasar MAfia A(2024)New Approach: Intelligent System-Based Object Detection in Smart Parking2024 Mediterranean Smart Cities Conference (MSCC)10.1109/MSCC62288.2024.10697023(1-6)Online publication date: 2-May-2024
https://doi.org/10.1109/MSCC62288.2024.10697023
Bouzbita SAfia A(2022)A Fuzzy Meta Model for Adjusting Ant Colony System ParametersProceedings of the 5th International Conference on Big Data and Internet of Things10.1007/978-3-031-07969-6_4(48-58)Online publication date: 3-Jul-2022
https://doi.org/10.1007/978-3-031-07969-6_4
Wu ZWu JZhao MFeng LLiu K(2020)Two-layered ant colony system to improve engraving robot’s efficiency based on a large-scale TSP modelNeural Computing and Applications10.1007/s00521-020-05468-4Online publication date: 18-Nov-2020
https://doi.org/10.1007/s00521-020-05468-4
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Index Terms

Parameter Adaptation for Ant Colony System Algorithm using Hidden Markov Model for TSP Problems
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning

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cover image ACM Other conferences

LOPAL '18: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications

May 2018

357 pages

ISBN:9781450353045

DOI:10.1145/3230905

Copyright © 2018 ACM.

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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Association for Computing Machinery

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Publication History

Published: 02 May 2018

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LOPAL '18

LOPAL '18: Theory and Applications

May 2 - 5, 2018

Rabat, Morocco

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LOPAL '18 Paper Acceptance Rate 61 of 141 submissions, 43%;

Overall Acceptance Rate 61 of 141 submissions, 43%

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Cited By

Boulaich MOhamouddou SMeliani HEnnasar MAfia A(2024)New Approach: Intelligent System-Based Object Detection in Smart Parking2024 Mediterranean Smart Cities Conference (MSCC)10.1109/MSCC62288.2024.10697023(1-6)Online publication date: 2-May-2024
https://doi.org/10.1109/MSCC62288.2024.10697023
Bouzbita SAfia A(2022)A Fuzzy Meta Model for Adjusting Ant Colony System ParametersProceedings of the 5th International Conference on Big Data and Internet of Things10.1007/978-3-031-07969-6_4(48-58)Online publication date: 3-Jul-2022
https://doi.org/10.1007/978-3-031-07969-6_4
Wu ZWu JZhao MFeng LLiu K(2020)Two-layered ant colony system to improve engraving robot’s efficiency based on a large-scale TSP modelNeural Computing and Applications10.1007/s00521-020-05468-4Online publication date: 18-Nov-2020
https://doi.org/10.1007/s00521-020-05468-4
Aoun OEl Afia ATalbi E(2020)A Cooperative Multi-swarm Particle Swarm Optimizer Based Hidden Markov ModelHeuristics for Optimization and Learning10.1007/978-3-030-58930-1_21(315-334)Online publication date: 16-Dec-2020
https://doi.org/10.1007/978-3-030-58930-1_21
El Afia ALalaoui MTalbi E(2020)Quaternion Simulated AnnealingHeuristics for Optimization and Learning10.1007/978-3-030-58930-1_20(299-314)Online publication date: 16-Dec-2020
https://doi.org/10.1007/978-3-030-58930-1_20
Lalaoui MEl Afia AChiheb R(2020)Dynamic Simulated Annealing with Adaptive Neighborhood Using Hidden Markov ModelHeuristics for Optimization and Learning10.1007/978-3-030-58930-1_11(167-182)Online publication date: 16-Dec-2020
https://doi.org/10.1007/978-3-030-58930-1_11
Mandal ADehuri S(2020)A Survey on Ant Colony Optimization for Solving Some of the Selected NP-Hard ProblemBiologically Inspired Techniques in Many-Criteria Decision Making10.1007/978-3-030-39033-4_9(85-100)Online publication date: 20-Jan-2020
https://doi.org/10.1007/978-3-030-39033-4_9
Afia ALalaoui MChiheb R(2019)A Self Controlled Simulated Annealing Algorithm using Hidden Markov Model State ClassificationProcedia Computer Science10.1016/j.procs.2019.01.024148(512-521)Online publication date: 2019
https://doi.org/10.1016/j.procs.2019.01.024
Bouzbita SEl Afia AFaizi R(2019)Adjusting Population Size of Ant Colony System Using Fuzzy Logic ControllerComputational Collective Intelligence10.1007/978-3-030-28374-2_27(309-320)Online publication date: 9-Aug-2019
https://doi.org/10.1007/978-3-030-28374-2_27
Lalaoui MAfia A(2018)A Fuzzy generalized simulated annealing for a simple assembly line balancing problemIFAC-PapersOnLine10.1016/j.ifacol.2018.11.48951:32(600-605)Online publication date: 2018
https://doi.org/10.1016/j.ifacol.2018.11.489

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