Swarm intelligence is one of the central focus areas in the study of metaheuristic algorithms. The effectiveness of these algorithms towards solving difficult problems has attracted researchers and practitioners. As a result, numerous type of this algorithm have been proposed. However, there is a heavy critics that some of these algorithms lack novelty. In fact, some of these algorithms are the same in terms of the updating operators but with different mimicking scenarios and names. The performance of a metaheuristic algorithm depends on how it balance the degree of the two basic search mechanisms, namely intensification and diversification. Hence, introducing novel algorithms which contributes to a new way of search mechanism is welcome but not for a mere repetition of the same algorithm with the same or perturbed operators but different metaphor. With this regard, it is ideal to have a framework where different custom made operators are used along with existing or new operators. Hence, this paper presents a swarm hyperheuristic framework, where updating operators are taken as low level heuristics and guided by a high level hyperheuristic. Different learning approaches are also proposed to guide the intensification and diversification search behaviour of the algorithm. Hence, a swarm hyperheuristic without learning (\({ SHH}1\)), with offline learning (\({ SHH}2)\) and with an online learning (\({ SHH}3\)) is proposed and discussed. A simulation based comparison and discussion is also presented using a set of nine updating operators with selected metaheuristic algorithms based on twenty benchmark problems. The problems are selected from both unconstrained and constrained optimization problems with their dimension ranging from two to fifty. The simulation results show that the proposed approach with learning has a better performance in general.

中文翻译：

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群过启发式框架

群智能是元启发式算法研究的重点领域之一。这些算法解决难题的有效性吸引了研究人员和从业人员。结果，已经提出了多种类型的该算法。但是，有很多批评家认为其中一些算法缺乏新颖性。实际上，这些算法中的某些算法在更新运算符方面是相同的，但是具有不同的模拟方案和名称。元启发式算法的性能取决于它如何平衡两种基本搜索机制（即强化和多样化）的程度。因此，欢迎引入有助于一种新的搜索机制方式的新颖算法，但不能仅仅重复使用具有相同或摄动算子但具有不同隐喻的相同算法。考虑到这一点，理想的是拥有一个框架，其中将不同的定制运算符与现有或新的运算符一起使用。因此，本文提出了一个群体超启发式框架，在该框架中，将更新运算符视为低级启发式，并以高级超启发式为指导。还提出了不同的学习方法，以指导算法的强化和多样化搜索行为。因此，一群没有学习的超启发式（还提出了不同的学习方法，以指导算法的强化和多样化搜索行为。因此，一群没有学习的超启发式（还提出了不同的学习方法，以指导算法的强化和多样化搜索行为。因此，一群没有学习的超启发式（\（{SHH} 1 \） ）中，用离线学习（\（{SHH} 2）\） ，并用一个在线学习（\（{SHH} 3 \） ）提出并讨论。还使用一组九个更新算子和基于二十个基准问题的选定元启发式算法，提出了基于仿真的比较和讨论。从无约束和有约束的优化问题中选择问题，它们的范围为2到50。仿真结果表明，该方法具有较好的学习效果。