Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/84896
標題: Application of data mining in a global optimization algorithm
關鍵字: Global optimization algorithm;Data mining;Evolution strategy;Sequential quadratic programming;Reduced search space;Hybrid search method
Project: Advances in Engineering Software, Volume 66, Page(s) 24-33.
摘要: 
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.
URI: http://hdl.handle.net/11455/84896
DOI: 10.1016/j.advengsoft.2012.11.019
Appears in Collections:機械工程學系所

Show full item record
 

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.