Solving the Euclidean Steiner Minimum Tree Using Cellular Stochastic Diffusion Search Algorithm

The Euclidean Steiner minimum tree problem is a classical NP-hard combinatorial optimization problem.Because of the intrinsic characteristic of the hard computability,this problem cannot be solved accurately by efficient algorithms up to now.Due to the extensive applications in real world,it is quite important to find some heuristics for it.The stochastic diffusion search algorithm is a newly population-based algorithm whose operating mechanism is quite different from ordinary intelligent algorithms,so this algorithm has its own advantage in solving some optimization problems.This paper has carefully studied the stochastic diffusion search algorithm and designed a cellular automata stochastic diffusion search algorithm for the Euclidean Steiner minimum tree problem which has low time complexity.Practical results show that the proposed algorithm can find approving results in short time even for the large scale size,while exact algorithms need to cost several hours.

Steiner最小树问题的量子蚁群算法

Steiner最小树问题是组合优化中一个经典的NP难题,本文在蚁群算法的基础上结合量子计算提出一种求解欧氏Steiner最小树问题的量子蚁群算法.将量子比特、量子逻辑门以及Grover量子算法引入到蚁群算法中去,有效提高了算法的全局搜索能力,搜索速度也有显著的提高.一系列数据实例计算与比较表明,量子蚁群算法较蚁群算法在Steiner最小树问题的求解上具有更好的性能.

欧氏Steiner最小树的Delaunay三角网混合智能求解方法

欧氏Steiner最小树问题是组合优化中一个经典的NP难题,在许多实际问题中有着广泛的应用.由于使用普通智能算法求解较大规模问题时,极易陷入拓扑结构的局部最优,因此,基于Delaunay三角网技术并结合智能算法的有关思想,设计了一种改进的混合型智能求解方法,可大幅度提高算法在寻找更好拓扑结构上的有效性.算法在Matlab环境下编程实现,经大量STEINLIB中的标准数据实例测试和验证,获得了满意的效果,为求解较大规模的欧氏Steiner最小树问题提供了新的有效方法.

三维欧氏Steiner最小树的Delaunay四面体网格混合智能算法

Steiner最小树问题是组合优化中经典的NP难题,在许多实际问题中有着广泛的应用,而三维欧氏Steiner最小树问题是对二维欧氏Steiner最小树问题的推广。由于三维欧氏Steiner树问题的求解非常困难,至今为止的相关成果较为少见。本文针对该问题,利用Delaunay四面体网格剖分技术,提出了一种混合型智能求解方法,不仅可以尽量避免拓扑结构陷入局部最优,且对较大规模的问题求解亦有良好的效果。算法在Matlab环境下编程实现,经实例测试,获得了满意的效果。