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
Numerical Recipes in Pascal: The Art of Scientific Computing
Numerical Recipes in Pascal: The Art of Scientific Computing
Using Experimental Design to Find Effective Parameter Settings for Heuristics
Journal of Heuristics
Model-Based Search for Combinatorial Optimization: A Comparative Study
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
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
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
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
Fine-Tuning algorithm parameters using the design of experiments approach
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
DOE-based parameter tuning for local branching algorithm
International Journal of Metaheuristics
Automatic (offline) configuration of algorithms
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A hybrid metaheuristic approach for the capacitated p-median problem
Applied Soft Computing
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
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This paper presents an in-depth Design of Experiments (DOE) methodology for the performance analysis of a stochastic heuristic. The heuristic under investigation is Max-Min Ant System (MMAS). for the Travelling Salesperson Problem (TSP). Specifically, the Response Surface Methodology is used to model and tune MMAS performance with regard to 10 tuning parameters, 2 problem characteristics and 2 performance metrics--solution quality and solution time. The accuracy of these predictions is methodically verified in a separate series of confirmation experiments. The two conflicting responses are simultaneously optimised using desirability functions. Recommendations on optimal parameter settings are made. The optimal parameters are methodically verified. The large number of degrees-of-freedom in the MMAS design are overcome with a Minimum Run Resolution V design. Publicly available algorithm and problem generator implementations are used throughout. The paper should therefore serve as an illustrative case study of the principled engineering of a stochastic heuristic.