Numerical recipes in Pascal: the art of scientific computing
Numerical recipes in Pascal: the art of scientific computing
American Journal of Mathematical and Management Sciences - Modern digital simulation methodology, III
A systematic procedure for setting parameters in simulated annealing algorithms
Computers and Operations Research
Future Generation Computer Systems
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Using Experimental Design to Find Effective Parameter Settings for Heuristics
Journal of Heuristics
Ant Colony Optimization
Design and Analysis of Experiments
Design and Analysis of Experiments
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Screening the parameters affecting heuristic performance
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An analysis of problem difficulty for a class of optimisation heuristics
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
Screening the parameters affecting heuristic performance
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Tuning the performance of the MMAS heuristic
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
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This research uses a Design of Experiments (DOE) approach to build a predictive model of the performance of a combinatorial optimization heuristic over a range of heuristic tuning parameter settings and problem instance characteristics. The heuristic is Ant Colony System (ACS) for the Travelling Salesperson Problem. 10 heurstic tuning parameters and 2 problem characteristics are considered. Response Surface Models (RSM) of the solution quality and solution time predicted ACS performance on both new instances from a publicly available problem generator and new real-world instances from the TSPLIB benchmark library. A numerical optimisation of the RSMs is used to find the tuning parameter settings that yield optimal performance in terms of solution quality and solution time. This paper is the first use of desirability functions, a well-established technique in DOE, to simultaneously optimise these conflicting goals. Finally, overlay plots are used to examine the robustness of the performance of the optimised heuristic across a range of problem instance characteristics. These plots give predictions on the range of problem instances for which a given solutionquality can be expected within a given solution time.