Analysis of global information sharing in hyper-heuristics for different dynamic environments

Optimisation methods designed for static environments do not perform as well on dynamic optimisation problems as purpose-built methods do. Hyper-heuristics show great promise in handling dynamic environment dynamics because hyper-heuristics adapt to their environment. Different classifications of dynamic environments describe change dynamics such as spatial change severity, temporal change severity, homogeneity of peak movement, etc. Previous studies show that different hyper-heuristic selection mechanisms perform differently across different types of dynamic environments. This study investigates three hyper-heuristic selection methods with different selection pressures and shows an inverse correlation with environment change severity.

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