Swarm hyperheuristic framework

Abstract

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.