Group mosquito host-seeking algorithm

doi:10.11772/j.issn.1001-9081.2014.04.1055

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (4): 1055-1059.DOI: 10.11772/j.issn.1001-9081.2014.04.1055

• Artificial intelligence • Previous Articles Next Articles

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:2014-04-29 Published:2014-04-01
Contact: FENG Xiang
Supported by:
National Natural Science Foundation;National Natural Science Foundation

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

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

Abstract

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.

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。

CLC Number:

TP18
TP301.6

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

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.

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Group mosquito host-seeking algorithm

群蚊子追踪算法

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