| Summary: | 碩士 === 大同大學 === 機械工程學系(所) === 99 === Differential evolution has been applied in many engineering fields, but the original differential algorithm can be used only on the real number optimization problems. It still has a difficulty in dealing with binary optimization problems due to the fact that the representation of design variable is a real-value type. In order to develop a differential evolution algorithm which can be suitable for both real-valued and binary optimization problems, a modified binary differential evolution is integrated with adaptive resonance theory neural network in this study. The clustering capability of adaptive resonance theory can maintain population diversity during evolution process and the common characteristics of classified group can accelerate the convergence. In order to understand the performance and the viability of developed framework, several test functions are utilized for demonstration by using same stopping criteria applied in the reference. The developed modified binary differential evolution with adaptive resonance theory is also used in structural topology optimization problems. From results, it is shown that adaptive resonance theory can improve the modified binary differential evolution algorithm in population diversity, convergence rate and search performance simultaneously in dealing with real-valued test functions and binary topology optimization problems.
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