多群落双向驱动协作搜索算法

Multi-community bidirectional drive collaborative search algorithm

针对复杂优化问题中数据混杂多变的特点,提出一种能够根据环境变化不断优化种群适应度的多群落双向驱动协作搜索算法。该算法在分析微粒群落特性的基础上,基于无向加权图建立了多群落协作网演化模型,该模型依据群落适应值的优劣程度对群落类型进行划分,并根据不同群落间的协作权重和群落节点响应度评估群落节点强度,由节点强度最大的群落引导整个协作网进化,改进传统群集智能算法面对复杂优化问题中环境变化的自适应性能缺陷;构建了一种多群落双向驱动的进化新模式,给出了多群落协作的异步并行搜索算法,实现了不同环境下群落内部与群落之间的并行进化,降低了数据分析中巨大的计算时空开销。实验结果表明,该方法面向混杂多变数据不断优化种群适应度,能够较快地适应环境变化,并在可接受的时间内得到精确解,为复杂优化问题的求解提供了有效手段。

Aiming at the hybrid variable characteristic of complex optimization problem,a multi-community bidirectional driven collaborative search method was proposed to optimize population fitness adaptively according to environmental changes.By analyzing the community characteristics of particles,the evolution model of multi-community cooperation network was established on the basis of undirected weighted graph,and the community types were divided according to the advantages and disadvantage of community fitness,in which the population node strength was evaluated by the cooperation weights and the node response between different communities.Faced with the environment changes of complex optimization problem,the adaptive performance of traditional swarm intelligence algorithm was improved by guiding the evolution of entire collaboration network from the largest node strength community.A multi-community bidirectional drive mode was constructed and its asynchronous parallel search algorithm was given,which realized the parallel evolution within community and among community and reduce the huge computation cost in the analysis of data.The experimental results showed that the presented method could rapidly adapt environmental changes of the hybrid data by optimizing the population fitness,and obtain the exact solution in an acceptable time.It provided useful means for solving the complex optimization problem.