Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many algorithms for continuous simulation optimization have been proposed, but the question of the number of replications at a specific point is always an issue. , instead of averaging replications of the objective function at a specific point (e.g., sample average),  average observed function evaluations from neighboring points. The Improving Hit-and-Run algorithm is modified to accommodate averaging in a ball of fixed radius, thus only sampling any point once. The computational results suggest an efficiency with single observations per sample point that simultaneously improves the estimation of the function value and samples closer to the optimum as the algorithm progresses.
['Seksan Kiatsupaibul', 'Robert L. Smith', 'Zelda B. Zabinsky']
I am writing a research paper on a idea Improving hit-and-run with single observations for continuous simulation optimization
[{"from": "human", "value": "I am writing a research paper on a idea Improving hit-and-run with single observations for continuous simulation optimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many algorithms for continuous simulation optimization have been proposed, but the question of the number of replications at a specific point is always an issue. , instead of averaging replications of the objective function at a specific point (e.g., sample average),  average observed function evaluations from neighboring points. The Improving Hit-and-Run algorithm is modified to accommodate averaging in a ball of fixed radius, thus only sampling any point once. The computational results suggest an efficiency with single observations per sample point that simultaneously improves the estimation of the function value and samples closer to the optimum as the algorithm progresses."}]