群蚊子追踪算法

Group mosquito host-seeking algorithm

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。

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.