Analysis of hyper-heuristic performance in different dynamic environments

Abstract

Optimisation methods designed for static environments do not perform as well on dynamic optimisation problems as purpose-built methods do. Intuitively, hyper-heuristics show great promise in handling dynamic optimisation problem dynamics because hyper-heuristics can select different search methods to employ at different times during the search based on performance profiles. Related studies use simple heuristics in dynamic environments and do not evaluate heuristics that are purpose-built to solve dynamic optimisation problems. This study analyses the performance of a random-based selection hyper-heuristic that manages meta-heuristics that specialise in solving dynamic optimisation problems. The performance of the hyper-heuristic across different types of dynamic environments is investigated and compared with that of the heuristics running in isolation and the same hyper-heuristic managing simple Gaussian mutation heuristics.