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Multivariate ant colony optimization in continuous search spaces

Published: 12 July 2008 Publication History

Abstract

This work introduces an ant-inspired algorithm for optimization in continuous search spaces that is based on the generation of random vectors with multivariate Gaussian pdf. The proposed approach is called MACACO -- Multivariate Ant Colony Algorithm for Continuous Optimization -- and is able to simultaneously adapt all the dimensions of the random distribution employed to generate the new individuals at each iteration. In order to analyze MACACO's search efficiency, the approach was compared to a pair of counterparts: the Continuous Ant Colony System (CACS) and the approach known as Ant Colony Optimization in en (ACOR). The comparative analysis, which involves well-known benchmark problems from the literature, has indicated that MACACO outperforms CACS and ACOR in most cases as the quality of the final solution is concerned, and it is just about two times more costly than the least expensive contender.

References

[1]
G. Bilchev and I. C. Parmee. The ant colony metaphor for searching continuous design spaces. In T. C. Fogarty, editor, Evolutionary Computing, AISB Workshop, volume 993 of Lecture Notes in Computer Science, pages 25--39. Springer, 1995.
[2]
G. E. P. Box and M. A. Muller. A note on the generation of random normal deviates. Annals. Math. Stat., 29:610--611, 1958.
[3]
F. O. de França, F. J. Von Zuben, and L. N. de Castro. Max min ant system and capacitated p-medians: Extensions and improved solutions. Informatica (Slovenia), 29(2):163--172, 2005.
[4]
M. Dorigo. Optimization, Learning and Natural Algorithms. PhD thesis, Politecnico di Milano, Italy,1992.
[5]
M. Dorigo and G. Di Caro. The ant colony optimization meta-heuristic. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pages 11--32. McGraw-Hill, London, 1999.
[6]
M. Dorigo and T. Stützle. The ant colony optimization metaheuristic: Algorithms, applications, and advances. In F. W. Glover and G. A. Kochenberger, editors, Handbook of Metaheuristics, pages 251--286. Kluwer Academic Press, 2003.
[7]
J. Dréo and P. Siarry. A new ant colony algorithm using the heterarchical concept aimed at optimization
[8]
of multiminima continuous functions. In M. Dorigo, G. D. Caro, and M. Sampels, editors, Ant Algorithms, volume 2463 of Lecture Notes in Computer Science, pages 216--221. Springer, 2002.
[9]
M. Guntsch and M. Middendorf. A population based approach for ACO. In S. Cagnoni, J. Gottlieb, E. Hart, M. Middendorf, and G. Raidl, editors, Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim, volume 2279 of LNCS, pages 72--81, Kinsale, Ireland, 3--4 2002. Springer--Verlag.
[10]
I. T. Hernádvölgyi. Generating random vectors from the multivariate normal distribution. Technical Report TR-98-07, University of Ottawa, Aug. 20 1998.
[11]
G. Marsaglia and W. W. Tsang. The ziggurat method for generating random variables. Journal of Statistical Software, 5(8):1--7, 2000.
[12]
N. Monmarché, G. Venturini, and M. Slimane. On how Pachycondyla apicalis ants suggest a new search algorithm. Future Generation Computer Systems, 16(8):937--946, 2000.
[13]
S. H. Pourtakdoust and H. Nobahari. An extension of ant colony system to continuous optimization problems. In M. Dorigo, M. Birattari, C. Blum, L. M. Gambardella, F. Mondada, and T. Stutzle, editors, ANTS Workshop, volume 3172 of Lecture Notes in Computer Science, pages 294--301. Springer, 2004.
[14]
Y.-W. Shang and Y.-H. Qiu. A note on the extended Rosenbrock function. Evolutionary Computation, 14(1):119--126, March 2006.
[15]
K. Socha and M. Dorigo. Ant colony optimization for continuous domains. European Journal of Operational Research, In Press, Corrected Proof, 2006.
[16]
T. Stützle and M. Dorigo. ACO algorithms for the quadratic assignment problem. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pages 33--50. McGraw-Hill, London, 1999.

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    cover image ACM Conferences
    GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
    July 2008
    1814 pages
    ISBN:9781605581309
    DOI:10.1145/1389095
    • Conference Chair:
    • Conor Ryan,
    • Editor:
    • Maarten Keijzer
    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: 12 July 2008

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    Author Tags

    1. ant colony optimization
    2. continuous optimization
    3. multivariate normal distribution

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    • (2019)Use of aggregation pheromone density for image segmentationPattern Recognition Letters10.1016/j.patrec.2009.03.00430:10(939-949)Online publication date: 6-Jan-2019
    • (2019)The differential ant-stigmergy algorithmInformation Sciences: an International Journal10.1016/j.ins.2010.05.002192(82-97)Online publication date: 6-Jan-2019
    • (2016)Definition and Experimental Validation of a Simplified Model for a Microgrid Thermal Network and its Integration into Energy Management SystemsEnergies10.3390/en91109149:11(914)Online publication date: 4-Nov-2016
    • (2011)A hybrid ant colony optimization for continuous domainsExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.02.15138:9(11072-11077)Online publication date: 1-Sep-2011
    • (2010)SamACOIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics10.1109/TSMCB.2010.204309440:6(1555-1566)Online publication date: 1-Dec-2010
    • (2010)An Ant Based Approach for Generating Procedural AnimationsProceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 0110.1109/ICTAI.2010.12(19-26)Online publication date: 27-Oct-2010
    • (2009)High‐dimensional real‐parameter optimization using the differential ant‐stigmergy algorithmInternational Journal of Intelligent Computing and Cybernetics10.1108/175637809109392462:1(34-51)Online publication date: 27-Mar-2009

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