Iterated local search(ILS)is used to construct the optimal experimental designs for multi-dimensional constrained spaces,in which the inner loop is based on the stochastic coordinate-exchange(SCE)algorithm.Every time ...Iterated local search(ILS)is used to construct the optimal experimental designs for multi-dimensional constrained spaces,in which the inner loop is based on the stochastic coordinate-exchange(SCE)algorithm.Every time a local optimal solution is found by the SCE algorithm,the perturbation operator is applied to it,and then a new solution is explored in the areas where the exchange of coordinates may produce improvement,so as to retain the features and attributes of the current optimal solution and avoid the defects of random restart.We implement the iterated local coordinate-exchange algorithm for experimental designs in the multi-dimensional constrained spaces.In addition,sensitivity analysis was conducted to analyze the impacts of the parameters on the performance of the proposed algorithm.Also we compared the performance of the proposed algorithm to the SCE algorithm using the random restart strategy.The analysis shows that the proposed algorithm is better than the SCE algorithm in terms of efficiency and quality,especially in the experimental designs for high-dimensional constrained space.展开更多
基金This work was supported by the National Natural Science Foundation of China(72171231).
文摘Iterated local search(ILS)is used to construct the optimal experimental designs for multi-dimensional constrained spaces,in which the inner loop is based on the stochastic coordinate-exchange(SCE)algorithm.Every time a local optimal solution is found by the SCE algorithm,the perturbation operator is applied to it,and then a new solution is explored in the areas where the exchange of coordinates may produce improvement,so as to retain the features and attributes of the current optimal solution and avoid the defects of random restart.We implement the iterated local coordinate-exchange algorithm for experimental designs in the multi-dimensional constrained spaces.In addition,sensitivity analysis was conducted to analyze the impacts of the parameters on the performance of the proposed algorithm.Also we compared the performance of the proposed algorithm to the SCE algorithm using the random restart strategy.The analysis shows that the proposed algorithm is better than the SCE algorithm in terms of efficiency and quality,especially in the experimental designs for high-dimensional constrained space.
