A hybrid global optimization algorithm is developed in this research. The probability of finding the globaloptimal solution is increased by reducing the search space. The activities of classification, association, andclustering in data mining are employed to achieve this purpose. The hybrid algorithm developed usesdata mining (DM), evolution strategy (ES) and sequential quadratic programming (SQP) to search forthe global optimal solution. For unconstrained optimization problems, data mining techniques are usedto determine a smaller search region that contains the global solution. For constrained optimization problems,the data mining techniques are used to find the approximate feasible region or the feasible regionwith better objective values. Numerical examples demonstrate that this hybrid algorithm can effectivelyfind the global optimal solutions for two benchmark test problems
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