research-article

A distributed ant-based algorithm for numerical optimization

Authors:

Peter Korošec,

Jurij ŠilcAuthors Info & Claims

BADS '09: Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems

Pages 37 - 44

https://doi.org/10.1145/1555284.1555291

Published: 19 June 2009 Publication History

Abstract

This paper presents a new, distributed approach to the numerical optimization problem. The algorithm is based on ant-stigmergy metaheuristics, where indirect coordination between the ants drives the search procedure towards the optimal solution. Indirect coordination offers a high degree of parallelism and therefore a straightforward distributed implementation. For the communication between processes a MPICH2 for Windows library is used. The cost-function evaluation is an important part of the numerical optimization and is usually realized as a black-box simulator. Therefore, an algorithm analysis according to the simulator's time complexity is discussed.

References

[1]

G. Bilchev and I. C. Parmee. The ant colony metaphor for searching continuous design spaces. In T. C. Fogarty, editor, Evolutionary Computing, volume 993 of Lecture Notes in Computer Science, pages 25--39, Sheeld, UK, April 3-4 1995.

Digital Library

[2]

V. Cutello, G. Narzisi, G. Nicosia, and M. Pavone. An immunological algorithm for global numerical optimization. In E.-G. Talbi, P. Liardet, P. Collet, E. Lutton, and M. Schoenauer, editors, Proceedings of the 7th International Conference on Artificial Evolution, EA 2005, volume 3871 of Lecture Notes in Computer Science, pages 284--295, Lille, France, 2006. Springer-Verlag.

Digital Library

[3]

K. Deb, A. Anand, and D. Joshi. A computationally efficient evolutionary algorithm for real-parameter optimization. Evolutionary Computation, 10(4):371--395, December 2002.

Digital Library

[4]

P. Delisle, M. Gravel, M. Krajecki, C. Gagné, and W. L. Price. Comparing parallelization of an ACO: Message passing vs. shared memory. In M. J. Blesa, C. Blum, A. Roli, andM. Samples, editors, Proceedings of the Second International Workshop on Hybrid Metaheuristics, HM 2005, volume 3636 of Lecture Notes in Computer Science, pages 1--12, Barcelona, Spain, August 29-30 2005. Springer-Verlag.

Digital Library

[5]

J. Kennedy and R. C. Eberhart. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, volume IV, pages 1942--1948, Perth, Australia, December 1995. IEEE Service Center, Piscataway, NJ.

[6]

P. Korošec and J. Šilc. The differential ant-stigmergy algorithm for large scale real-parameter optimization. In M. Dorigo, M. Birattari, C. Blum, M. Clerc, T. Stützle, and A. F. T. Winfield, editors, Ant Colony Optimization and Swarm Intelligence, Proceedings of the 6th International Workshop on Ant Colony Optimization and Swarm Intelligence, volume 521 of Lecture Notes in Computer Science, pages 413--414, Brussels, Belgium, September 22-24 2008. Springer Verlag.

Digital Library

[7]

P. Korošec, J. Šilc, K. Oblak, and F. Kosel. The differential ant-stigmergy algorithm: An experimental evaluation and a real-world application. In Proceedings of the 2007 Congress on Evolutionary Computation (CEC2007), volume 1, pages 157--164, Singapore, September 25-28 2007. IEEE, Piscataway.

[8]

P. Korošec, J. Šilc, K. Oblak, and F. Kosel. Optimizing the shape of an impeller using the differential ant-stigmergy algorithm. In R. Wyrzykowski, J. Dongarra, K. Karczewski, and J. Wasniewski, editors, Parallel Processing and Applied Mathematics, 7th International Conference, PPAM 2007, Gdansk, Poland, September 2007, Revised Selected Papers, volume 4967 of Lecture Notes in Computer Science, pages 520--529. Springer-Verlag, 2008.

Digital Library

[9]

Y. Lin, H. -C. Cai, J. Xiao, and J. Zhang. Pseudo parallel ant colony optimization for continuous functions. In Proceedings of the Third International Conference on Natural Computation (ICNC 2007), volume 4, pages 494--500, Haikou, Hainan, China, August 24-27 2007. IEEE Computer Society.

Digital Library

[10]

M. Randall and A. Lewis. A parallel implementation of ant colony optimization. Journal of Parallel and Distributed Computing, 62(9):1421--1432, 2002.

Digital Library

[11]

J. Šilc and P. Korošec. The distributed stigmergic algorithm for multi-parameter optimization. In R. Wyrzykowski, J. Dongarra, N. Meyer, and J. Wasniewski, editors, Parallel Processing and Applied Mathematics, 6th International Conference, PPAM 2005, Pozna ´n, Poland, September 11-14, 2005. Revised Selected Papers, volume 3911 of Lecture Notes in Computer Science, pages 92--99. Springer-Verlag, 2006.

Digital Library

[12]

R. Storn and K. V. Price. Differential evolution - a fast and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4):341--359, 1997.

Digital Library

[13]

T. Stützle. Parallelization strategies for ant colony optimization. In A. E. Eiben, T. Bäck, H.-P. Schwefel, and M. Schoenauer, editors, Proceedings of the Fifth International Conference on Parallel Problem Solving from Nature (PPSN V), volume 1498 of Lecture Notes in Computer Science, pages 722--741, Amsterdam, The Nederlands, September 27-30 1998. Springer-Verlag.

Digital Library

[14]

E.-G. Talbi, O. Roux, C. Fonlupt, and D. Robillard. Parallel ant colonies for the quadratic assignment problem. Future Generation Computer Systems, 17(4):441--449, 2001.

Digital Library

[15]

K. Taškova, P. Korošec, and J. Šilc. A distributed multilevel ant colonies approach. Informatica, 32(3):307--317, 2008.

[16]

A. H. Wright. Genetic algorithms for real parameter optimization. InG. J. E. Rawlins, editor, Foundations of Genetic Algorithms-1, pages 205--218, San Mateo, CA, 1991. Morgan Kaufman.

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A distributed ant-based algorithm for numerical optimization

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Published In

cover image ACM Conferences

BADS '09: Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems

June 2009

114 pages

ISBN:9781605585840

DOI:10.1145/1555284

Program Chairs:
Gianluigi Folino
ICAR-CNR, National Research Council, Italy
,
Natalio Krasnogor
University of Nottingham, United Kingdom
,
Carlo Mastroianni
ICAR-CNR, National Research Council, Italy
,
Franco Zambonelli
Università di Modena e Reggio Emilia, Italy

Copyright © 2009 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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Published: 19 June 2009

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ICAC '09: 6th International Conference on Autonomic Computing

June 19, 2009

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Cited By

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https://doi.org/10.1515/mt-2024-0099
Jiang YYu LMa JHu YFan GLi HHui P(2024)Digital Mustard Garden: Revitalizing Freehand-ink-painting Teaching through Artistic ParticipationProceedings of the 17th International Symposium on Visual Information Communication and Interaction10.1145/3678698.3687184(1-8)Online publication date: 11-Dec-2024
https://dl.acm.org/doi/10.1145/3678698.3687184
Qaddoura RYounes MBoukerche A(2024)Towards optimal tuned machine learning techniques based vehicular traffic prediction for real roads scenariosAd Hoc Networks10.1016/j.adhoc.2024.103508161(103508)Online publication date: Aug-2024
https://doi.org/10.1016/j.adhoc.2024.103508
Qaddoura RFaris H(2024)Evolving random weight neural networks based on oversampled-segmented examples for IoT intrusion detectionThe Journal of Supercomputing10.1007/s11227-024-06071-380:11(16393-16427)Online publication date: 1-Jul-2024
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Qaddoura RFaris HAljarah ICastillo P(2021)EvoCluster: An Open-Source Nature-Inspired Optimization Clustering FrameworkSN Computer Science10.1007/s42979-021-00511-02:3Online publication date: 31-Mar-2021
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Qaddoura RFaris HAljarah ICastillo P(2020)EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework in PythonApplications of Evolutionary Computation10.1007/978-3-030-43722-0_2(20-36)Online publication date: 9-Apr-2020
https://doi.org/10.1007/978-3-030-43722-0_2
Korošec PŠilc JVajteršic MKutil R(2011)A Shared-Memory ACO-Based Algorithm for Numerical OptimizationProceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum10.1109/IPDPS.2011.176(352-357)Online publication date: 16-May-2011
https://dl.acm.org/doi/10.1109/IPDPS.2011.176

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