Parallel ant colony optimisation algorithm for continuous domains on graphics processing unit

被引：0

作者：

机构：

[1] Department of Automation, Nankai University, Tianjin

来源：

Wang, C. (wangchen11@mail.nankai.edu.cn) | 1600年 / Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland卷 / 04期

关键词：

CACO; Compute unified device architecture; Continuous ant colony optimisation; CUDA; GPU; Graphic processing unit; Parallel computing;

D O I：

10.1504/IJCSM.2013.057252

中图分类号：

学科分类号：

摘要：

A novel parallel approach to run continuous ant colony optimisation (CACO) algorithm on graphic processing unit (GPU) is presented in this paper for solving large scale continuous optimisation problem. CACO which is an extension to continuous domains from standard ACO is a kind of population-based meta-heuristics in essence. The mechanism ofalgorithm is described in detail. Its parallel implementation on compute unified devicearchitecture (CUDA) is proposed in our work. The experiment results on actual hardware to optimise many-dimensions test functions are given. The results and analyses show theexcellent performance of algorithm. Copyright © 2013 Inderscience Enterprises Ltd.

引用

页码：231 / 241

页数：10

共 17 条

[1]

Bilchev G., Parmee I.C., The ant colony metaphor for searching continuous design spaces, Proceedings of the AISB Workshop on Evolutionary Computation, LNCS, pp. 25-39, (1995)

[2]

Cecilia J.M., Garcia J.M., Ujaldon M., Nisbet A., Amos M., Parallelization strategies for ant colony optimisation on GPUs, IEEE International Parallel & Distributed Processing Symposium, pp. 339-347, (2011)

[3]

Colomi A., Dorigo M., Maniezzo V., Distributed optimization by ant colonies, Proceedings of the First European Conference on Artificial Life, pp. 134-142, (1992)

[4]

Dorigo M., Optimization, Learning and Natural Algorithms, (1992)

[5]

Dorigo M., Gambardella L.M., Ant colonies for the travelling salesman problem, BioSystems, 43, 2, pp. 73-81, (1997)

[6]

Dorigo M., Maniezzo V., Colorni A., Positive feedback as a search strategy, Technical Report, pp. 91-106, (1991)

[7]

Dre'o J., Siarry P., Continuous interacting ant colony algorithm based on dense hierarchy, Future Generation Computer Systems, 20, 5, pp. 841-856, (2004)

[8]

Gao Y., Wang Y., Pei Z., An improved particle swarm optimisation for solving generalised travelling salesman problem, Int. J. of Computing Science and Mathematics, 3, 4, pp. 385-393, (2012)

[9]

Ma C., Li Y., He R., Duan G., Sun L., Qi B., New optimisation model and fuzzy adaptive weighted genetic algorithm for hazardous material transportation, Int. J. of Computing Science and Mathematics, 3, 4, pp. 341-352, (2012)

[10]

Mussi L., Daolio F., Cagnoni S., Evaluation of parallel particle swarm optimization algorithms within the CUDA™ architecture, Information Sciences, 181, 20, pp. 4642-4657, (2011)

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