SEQuence-Analysis Based Hyperheuristics (SEQAH) for Real-World Optimisation Problems

选择性超级搜索是一组优化技术，通过选择较低级别的启发式操作（例如变异，交叉和复制）的组合，在优化运行期间有效地优化搜索算法。它们在元算法（例如进化算法）之上的层次上运行，因此能够通过修改应用于搜索问题的元算法来对搜索空间的变化做出反应。传统的选择性超并行算法在确定下一个选择的启发式算法时，考虑单个启发式算法和启发式算法对的性能。该项目将开发新的方法，称为基于序列分析的hyperproliferistics（SEQAH），并将研究使用序列分析技术，从其他计算领域，如生物信息学和自然语言处理，以确定启发式选择。MQAH方法将搜索过程记录为启发式应用和性能对的序列，并将处理此信息以通知下一个启发式应用于优化的选择。这将允许该技术自动选择应用于给定问题的最佳算法-有效地将算法调整到新的优化问题类型，而不管底层应用领域如何。通过从一组算法中进行选择，所述MQAH技术可以联合收割机组合普通启发式操作（例如变异和交叉）与更多的问题特定的算法，如人类设计的“经验法则”，成为一个连贯的算法，能够产生接近最佳的解决方案，在更少的计算时间。开发的技术将进行测试的问题，从文献和一套真实的-水分配优化方面的世界性问题，包括大型水系统的设计、修复和运行。这些系统的优化有可能在可靠性和水质方面提供更好的服务，并减少未来为全国各地家庭提供清洁，安全饮用水的成本和环境影响。该MQAH技术也有可能扩展到水行业以外，并应适用于许多应用领域的任何数量的优化问题，由于其能够适应新的问题空间在线。

Selective hyperheuristics are a set of optimisation techniques that effectively optimise the search algorithm during an optimisation run by selecting combinations of lower level heuristic operations (e.g. mutation, crossover & replication). They operate at the level above metaheuristics (e.g. evolutionary algorithms) and are thus able to react to changes in the search space by modifying the heuristics that are applied to the search problem. Traditional selective hyperheuristics consider single heuristics and heuristic pair performance when determining the heuristic to select next. This project will develop new methods known as a sequence analysis based hyperheuristics (SEQAH) and will investigate the use of sequence analysis techniques, taken from other computational domains such as bioinformatics and natural-language processing, to determine heuristic selection. SEQAH methods will record the search process as a sequence of pairs of heuristic application and performance, and will process this information to inform the selection of the next heuristic to apply in the optimisation. This will allow the technique to automatically select the best heuristics to apply for a given problem - effectively tuning the algorithm to new optimisation problem types, regardless of the underlying application area. By selecting from a set of heuristics, the SEQAH techniques can combine ordinary heuristic operations (e.g. mutation and crossover) with more problem-specific heuristics such as human-designed 'rules-of-thumb' into one coherent algorithm that is able to generate near-optimal solutions in less computational time.The developed techniques will be tested on problems from the literature and a suite of real-world problems in water distribution optimisation including the design, rehabilitation and operation of large-scale water systems. The optimisation of these systems has the potential to offer improved services in terms of reliability and water quality and to reduce the future cost and environmental impact of providing clean, safe drinking water to homes across the country. The SEQAH technique also has the potential to extend beyond the water industry and should be applicable to any number of optimisation problems in many application areas due to its ability to adapt to new problem spaces online.