A Survey of Hyper-heuristics for Dynamic Optimization Problems

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part A

“…Therefore, selecting a proper set of components for all environments of such DOPs may be impossible. One way to address this challenge is to use hyperheuristic approaches [19], [136], [137], where the algorithm tries to choose the most proper set of components according to the current performance of the DOA.…”

“…To address the need for more detailed reviews, some surveys have narrowed their covered topics to specific areas to provide more technical details. For example, [18] covers the single class of self-adaptive approaches, [19] reviews hyperheuristics for DOPs, [14] and [15] focus on multi-objective DOPs, and [12] reviews combinatorial DOPs. Following the same line of work, in this two-part survey, we focus on single-objective unconstrained continuous DOPs.…”

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

“…Te best-performing optimiser will earn more probability to run in the next iterations, while the other optimisers will be activated less frequently, eventually until their corresponding probability is null. (vi) Noisy/dynamic optimisation: It will be useful to investigate new compact optimisation schemes (for instance, with new distributions or new sampling mechanisms) to deal with noisy functions or Dynamic Optimisation Problems (DOPs) [328,329] (i.e., problems where the search space changes over time). Concerning noisy optimisation, apart from some works on cGA [240,[330][331][332], the only few works dealing with noise are based on rcGA [333], cDE [334], and a compact EDA framework [335].…”

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part B

“…An important point here is that if the characteristics of a DOP change over time (e.g., it becomes unpredictable while it used to be predictable in the previous environments), choosing a proper set of components becomes difficult or sometimes even impossible. In such DOPs, hyperheuristic approaches can be used in DOAs to choose the best set of components depending on the current status of the problem [251], [252]. Hyperheuristic approaches can also be used for selecting the optimization update rules (e.g., update rules of PSO or DE) [253].…”