超启发式算法

Ochoa在用图染色算法求解课程表问题时，对超启发式算法空间进行了适应度地貌分析。

Ochoa conducted a fitness landscapes analysis on a hyper-heuristic search space by using graph coloring heuristics for timetabling .

文中建议利用这些特征，设计出更高效的超启发式算法。

We can use these futures to design more efficient hyper-heuristics .

我们实现了已经提出的基于模拟退火的超启发式算法，并把它作为基准进行对比。

We employ an existing simulated annealing based hyper-heuristic as a baseline .

超启发式算法的目标是设计一种选取启发式算子的启发式策略，以解决复杂多样的问题。

The goal of hyper-heuristics is to design heuristics to choose heuristics for solving complex problems .

正是基于这些原因，至今为止还没有人在扰动型超启发式算法空间进行过适应度地貌分析。

Based on these reasons , there is no research on perturbative hyper-heuristics space by using fitness landscapes analysis so far .

最终结论证实超启发式算法空间中的最优解被包围在众多的局部最优解之中，而不是孤立于地貌空间。

The result confirmed that the optimal solution was not isolated in the space , but surrounded by many local optimal solutions .

本文所用的一些方法，有效的减小了初始解对扰动型超启发式算法的影响，并寻求用多个解的适应度的平均值作为一个启发式算法序列的适应度。

Some methods of this paper use to reduce the impact of initial solution on perturbative hyper-heuristics , and we seek to use the average of multiple solutions fitness as the fitness of heuristic algorithm sequence .

在传感器管理中，传感器的选择算法计算是目前需求量最大的问题，采用超启发式算法降低传感器选择算法的复杂度计算。

The algorithms of selecting sensor sets in the sensor manager are too computationally demanding to be implemented in many systems . This paper studies the use of super-heuristic algorithm in sensor selection to reduce the computational complexity .

适应度地貌分析已经在构造型超启发式算法空间上得到了应用，并发现了其地貌空间的高中立性、位置偏移，以及全空间的凸面、大坑结构等特征。

Fitness landscapes analysis has been applied to analyze the structure of constructive hyper-heuristics space , which reveals these landscapes have a high neutrality and positional bias . They also have the feature of a globally convex or big valley structure .

与其他常用的超启发式搜索算法如禁忌搜索和模拟退火的比较表明，GLS在此类问题的求解质量、求解速度和算法鲁棒性方面具有较好的综合性能。

As compared with ( other ) ( common ) used meta-heuristics such as Tabu Search and Simulated Annealing , Guided Local Search shows notable ( better ) ( performance ) as to solution quality , solving rapidity and algorithm robustness .

超启发式传感器选择算法

Super-Heuristic Algorithm in Selecting Sensor Sets