For many structural optimization problems, it is hard or even impossible to find the global optimum solutionowing to unaffordable computational cost. An alternative and practical way of thinking is thus proposed inthis research to obtain an optimum design which may not be global but is better than most local optimumsolutions that can be found by gradient-based search methods. The way to reach this goal is to find asmaller search space for gradient-based search methods. It is found in this research that data mining canaccomplish this goal easily. The activities of classification, association and clustering in data mining areemployed to reduce the original design space. For unconstrained optimization problems, the data miningactivities are used to find a smaller search region which contains the global or better local solutions. Forconstrained optimization problems, it is used to find the feasible region or the feasible region with betterobjective values. Numerical examples show that the optimum solutions found in the reduced design spaceby sequential quadratic programming (SQP) are indeed much better than those found by SQP in the originaldesign space. The optimum solutions found in a reduced space by SQP sometimes are even better than thesolution found using a hybrid global search method with approximate structural analyses
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