CUSTOMHyS: Customising Optimisation Metaheuristics via Hyper-heuristic Search
Jorge M. Cruz‐Duarte, Iván Amaya, José Carlos Ortíz-Bayliss, Hugo Terashima‐Marín, Yong Shi
Abstract
There is a colourful palette of metaheuristics for solving continuous optimisation problems in the literature. Unfortunately, it is not easy to pick a suitable one for a specific practical scenario. Moreover, oftentimes the selected metaheuristic must be tuned until finding adequate parameter settings. Therefore, this work presents a framework based on a hyper-heuristic powered by Simulated Annealing for tailoring population-based metaheuristics. To do so, we recognise search operators from well-known techniques as building blocks for new ones. The presented framework comprises six main modules coded in Python, which can be used independently, and which help explore new metaheuristics.