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Genetic Algorithms (GAs) are efficient methods for search and optimization problems. On the other hand,there are some problems associated with the premature convergence to local optima of the multimodal function, whichhas multi peaks. The problem is related to the lack of genetic diversity of the population to cover the search spaces sufficiently. A sharing and crowding method were introduced. This paper proposed strategies to improve the convergence speed and the convergence to the global optimum for solving the multimodal optimization function. These strategies included the random generated sub-population that were well-distributed and spread widely through search spaces. The results of the simulation verified the effects of the proposed method. Key Words : Convergence Speed, Genetic Diversity, Genetic Operator, Global Optimum, Multimodal Function * Corresponding Author : Hong-Kyu Lee (Korea University of Technology and Education) Tel: +82-41-560-1162 email: hongkyu@koratech.ac.kr Received July 3, 2014 Revised (1st August 28, 2014, 2nd September 3, 2014) Accepted October 10, 2014 |
