Local Search Using Orthogonal Design of Experiment
نویسنده
چکیده
The local search with orthogonal design of experiment in its neighborhood determination (ODLS) outperforms the local search with the conventional neighborhood when the objective function includes noise. This models practical optimization problems that contains uncontrolled or unobserved variables. ODLS is robust and e cient since it shares all evaluations for direction determination of each variable. We illustrate the characteristics and demonstrate its performance in simple quadratic function plus random noise, and discuss their improved parallel processing capability from the conventional local search.
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