Nonparametric Comparison Of Two Dynamic Parameter Setting Methods In A Meta-Heuristic Approach

The use of meta-heuristics is very common when solving a combinatorial problem in practice. Some approaches provide very good quality solutions in a short amount of computational time, however the parameters must be set before solving the problem which could require much time. This paper investigates the problem of setting parameters using a typical meta-heuristic called Meta-RaPS (Metaheuristic for Randomized Priority Search.). Meta-RaPS is a promising meta-heuristic optimization method that has been applied to different types of combinatorial optimization problems and achieved very good performance compared to other meta-heuristic techniques. To solve a problem, Meta-RaPS uses two well-defined stages at each iteration: construction and local search. After a number of iterations, the best solution is reported. Meta-RaPS performance depends on the fine tuning of two parameters: the priority percentage and restriction percentage, which are used during the construction stage. This paper presents two different dynamic parameter setting methods to set Meta-RaPS parameters while at the same time a solution is being found. To compare these two approaches, nonparametric statistic approaches are utilized since the distribution of solutions is not normal. Results from both these dynamic parameter setting methods are reported