群蚊子追踪算法

doi:10.11772/j.issn.1001-9081.2014.04.1055

计算机应用 ›› 2014, Vol. 34 ›› Issue (4): 1055-1059.DOI: 10.11772/j.issn.1001-9081.2014.04.1055

群蚊子追踪算法

刘晓婷,冯翔,虞慧群

华东理工大学信息科学与工程学院,上海 200237

收稿日期:2013-10-25 修回日期:2013-12-25 出版日期:2014-04-01 发布日期:2014-04-29
通讯作者: 冯翔
作者简介:刘晓婷(1989-),女,山东烟台人,硕士研究生,CCF会员,主要研究方向:分布并行计算、计算机网络;
冯翔(1977-),女,湖北武汉人,教授,博士,CCF会员,主要研究方向:分布并行计算、人工智能、网络通信;
虞慧群(1967-),男,江苏溧阳人,教授,博士,CCF会员,主要研究方向:可信计算、云计算。
基金资助:
国家自然科学基金资助项目;国家自然科学基金资助项目;中央高校基本科研业务费资助项目

Group mosquito host-seeking algorithm

LIU Xiaoting,FENG Xiang,YU Huiqun

School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

Received:2013-10-25 Revised:2013-12-25 Online:2014-04-01 Published:2014-04-29
Contact: FENG Xiang
Supported by:
National Natural Science Foundation;National Natural Science Foundation

摘要/Abstract

摘要：

为利用高性能计算平台解决大规模复杂性问题，提出群蚊子追踪算法(GMHSA)。GMHSA是受到蚊子吸血行为的启发，以信息动力学为基础而提出的智能优化算法，涉及最大最小公平性及群体交互行为。利用群体分类机制，引入决策权概念，在整个种群中选择领导群体。利用领导力函数进行博弈，保持自身优越性，同时摆脱局部最优解。通过旅行商问题(TSP)对该算法进行测试，与其他智能优化算法进行对比，16节点并行实验中其加速比最高能达到158，接近线性加速比；而且GMHSA模型可直接用于运输问题等实际优化问题。结果表明GMHSA具有高度并行性及扩展性，是一种解决涉及行为的复杂优化问题的有效方法。

Abstract:

Concerning the optimization of the overall complexity problem on the high-performance computing platform, a new algorithm named Group Mosquito Host-Seeking Algorithm (GMHSA) was proposed. GMHSA was an intelligent optimization algorithm inspired by mosquitoes sucking blood behavior. It involved max-min fairness and group interaction behavior. The producer group was chosen according to the concept of leader decision and the leadership functions were constructed to make each group maintain their own superiority as well as getting rid of local optimal solution. The algorithm was tested by Traveling Salesman Problem (TSP) and compared with other swarm intelligent algorithms. In the parallel experiment of 16 nodes, the speedup of GMHSA was 15.8, which was nearly linear speedup. Moreover, it could be directly used to solve transport problems and other practical optimal problems. The results indicate that GMHSA has highly parallelism and scalability, and it is an effective measurement for solving complex optimal problems involving behavior.

中图分类号:

TP18
TP301.6

刘晓婷冯翔虞慧群. 群蚊子追踪算法[J]. 计算机应用, 2014, 34(4): 1055-1059.

LIU Xiaoting FENG Xiang YU Huiqun. Group mosquito host-seeking algorithm[J]. Journal of Computer Applications, 2014, 34(4): 1055-1059.

参考文献

［1］OKUYAMA T, OKITA M, ABE T, et al.Accelerating ODE-based simulation of general and heterogeneous biophysical models using a GPU ［J/OL］. IEEE Transactions on Parallel and Distributed Systems ［2013-11-20］. http://www.computer.org/csdl/trans/td/preprint/06577372-abs.html.
［2］VACCARO A, POPOV M, VILLACCI D, et al.An integratedframework for smart microgrids modeling, monitoring, control, communication, and verification ［J］. Proceedings of the IEEE, 2011, 99(1): 119-132.
［3］BALLARD G, DEMMEL J, HOLTZ O, et al.Communication-optimal parallel algorithm for Strassen's matrix multiplication ［C］//Proceedings of the 24th ACM Symposium on Parallelism in Algorithms and Architectures. New York: ACM, 2012: 193-204.
［4］HU T, HARDING S, BANZHAF W. Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm ［J］. Genetic Programming and Evolvable Machines, 2010, 11(2): 205-225.
［5］LI J, CHI Z, WAN D. Parallel genetic algorithm based on fine-grained model with GPU-accelerated ［J］. Control and Decision, 2008, 23(6): 697-700. (李建明,迟忠先,万单领.一种基于GPU加速细粒度并行遗传算法的实现方法［J］. 控制与决策, 2008, 23(6): 697-700.)
［6］LI J, ZHANG L, LIU L. A parallel immune algorithm based on fine-grained model with GPU-acceleration ［C］// ICICIC 2009: Proceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control. Piscataway: IEEE, 2009: 683-686.
［7］ZHAO J, LIU Q, WANG W, et al.A parallel immune algorithm for traveling salesman problem and its application on cold rolling scheduling ［J］. Information Sciences, 2011, 181(7): 1212-1223.
［8］MEDINA-RODRIGUEZ N, MONTIEL-ROSS O, SEPULVEDA R, et al.Tool path optimization for computer numerical control machines based on parallel ACO ［J］. Engineering Letters, 2012, 20(1): 101.
［9］BONABEAU E, DORIGO M, THERAULAZ G. Swarm intelligence: from natural to artificial systems［M］. New York: Oxford University Press, 1999.
［10］BANDYOPADHYAY S K, BHATTACHARYYA D, DAS P. Mosquito attack optimization ［J］. Ubiquity,2006,7(33):Article No. 9.
［11］FENG X, LAU F C M, YU H. A novel bio-inspired approach based on the behavior of mosquitoes ［J］. Information Sciences, 2013, 233: 87-108.
［12］KENNEDY J, EBERHART R. Particle swarm optimization ［C］// Proceedings of the 1995 IEEE International Conference on Neural Networks. Piscataway: IEEE, 1995, 4: 1942-1948.
［13］WEISSING F J. Born leaders ［J］. Nature, 2011, 474(7351): 288-289.
［14］HE S, WU Q H, SAUNDERS J R. Group search optimizer: an optimization algorithm inspired by animal searching behavior ［J］. IEEE Transactions on Evolutionary Computation, 2009, 13(5): 973-990.
［15］DORIGO M, Di CARO G. Ant colony optimization: a new meta-heuristic ［C］// CEC 99: Proceedings of the 1999 Congress on Evolutionary Computation. Piscataway: IEEE, 1999, 2: 1470-1777.
［16］KARABOGA D, BASTURK B. A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm ［J］. Journal of global optimization, 2007, 39(3): 459-471.
［17］DORIGO M, Di CARO G, SAMPELS M. Ant algorithms, Proceedings of the Third International Workshop, LNCS 2463 ［C］. Berlin: Springer-Verlag, 2002.
［18］LI J, ZHANG L, LIU L. A parallel immune algorithm based on fine-grained model with GPU-acceleration ［C］// ICICIC '09: Proceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control. Washington, DC: IEEE Computer Society, 2009: 683-686.
［19］ZHAO J, LIU Q, WANG W, et al.A parallel immune algorithm for traveling salesman problem and its application on cold rolling scheduling ［J］. Information Sciences: an International Journal, 2011, 181(7): 1212-1223.

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群蚊子追踪算法

Group mosquito host-seeking algorithm

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