COMPARISON OF SAMPLING METHODS FOR ALGORITHM CONFIGURATION PROBLEM: A CASE FOR TUNING DIFFERENTIAL ANT-STIGMERGY (DASA) ALGORITHM PARAMETERS

Abstract Metaheuristic and heuristic methods, which are effective tools for solving difficult computational problems, have many design parameters, and selecting their values affects their performance. A good selection of the parameter values can increase their performance. In this study, we compared eight different sampling methods, used for tuning the five control parameters of the Differential Ant-Stigmergy Algorithm (DASA). Although most of the proposed parameter tuning methods in the literature use sampling methods to initialize or select candidate parameter vectors, we used them as individual tuning methods in this study. This is the first study to use Latin Hypercube Hammersley Sampling (LHHS) for algorithm configuration problems. In the first part of this study, the performance of a large set of DASA parameter settings, obtained using different sampling methods, was evaluated on the Sphere function optimization problem with respect to two dimensions of this function. In the second part of the experiment, we compared the performance of these differently tuned versions of DASA on the first seven noiseless benchmark test functions of the Black-Box Optimization Benchmarking 2010 workshop. The results of our research demonstrated that the best parameter vector obtained with the LHHS method had a better performance than the default parameter value of the DASA and other best parameter values obtained with other methods (it has the best-ranking amongst the all compared method). Furthermore, according to our results, LHHS can produce promising results when used for the initialization of other state-of-art algorithm configuration methods.