Instance‐specific multi‐objective parameter tuning based on fuzzy logic

Applied to the symmetric Travelling Salesman Problem and the meta‐heuristic Guided Local Search, the approach is consistently faster than a traditional non‐instance‐specific parameter tuning strategy without significantly affecting solution quality; optimised for speed, computational times are shown to be on average 20 times faster while producing solutions of similar quality. A number of interesting areas for further research are discussed. Finding good parameter values for meta‐heuristics is known as the parameter setting problem. A new parameter tuning strategy, called IPTS, is proposed that is a novel instance‐specific method to take the trade‐off between solution quality and computational time into consideration. Two important steps in the method are an a priori statistical analysis to identify the factors that determine heuristic performance in both quality and time for a specific type of problem, and the transformation of these insights into a fuzzy inference system rule base which aims to return parameter values on the Pareto‐front with respect to a decision maker’s preference.