Hyperheuristics rise as powerful techniques that get good results in less computational time than exact methods like dynamic programming or branch and bound. These exact methods promise the global best solution, but with a high computational time. In this matter, hyperheuristics do not promise the global best solution, but they promise a good solution in a lot less computational time. On the contrary, fuzzy logic provides the tools to model complex problems in a more natural way. With this in mind, this paper proposes a fuzzy hyperheuristic approach, which is a combination of a fuzzy inference system with a selection hyperheuristic. The fuzzy system needs the optimization of its fuzzy rules due to the lack of expert knowledge; indeed, traditional hyperheuristics also need an optimization of their rules. The fuzzy rules are optimized by genetic algorithms, and for the rules of the traditional methods, we use particle swarm optimization. The genetic algorithm will also reduce the number of fuzzy rules, in order to find the best minimal fuzzy rules, whereas traditional methods already use very few rules. Experimental results show the advantage of using our approach instead of a traditional selection hyperheuristic in 3200 instances of the 0/1 knapsack problem.

中文翻译：

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通过模糊逻辑增强背包问题的超启发式方法

超启发式技术作为一种强大的技术而兴起，与诸如动态编程或分支定界之类的精确方法相比，它可以在更少的计算时间内获得良好的结果。这些精确的方法可提供全局最佳解决方案，但计算时间较长。在这个问题上，超启发式算法不能保证全局最佳解决方案，但是它们可以在更少的计算时间内保证好的解决方案。相反，模糊逻辑提供了更自然地建模复杂问题的工具。考虑到这一点，本文提出了一种模糊超启发式方法，该方法是将模糊推理系统与选择超启发式方法相结合。由于缺乏专业知识，模糊系统需要对其模糊规则进行优化。确实，传统的启发式算法还需要优化其规则。通过遗传算法对模糊规则进行了优化，对于传统方法的规则，我们采用了粒子群算法。遗传算法还将减少模糊规则的数量，以找到最佳的最小模糊规则，而传统方法已经使用了很少的规则。实验结果表明，在3200个0/1背包问题实例中，使用我们的方法代替传统的选择启发式方法具有优势。