本論文提出了基於萊維飛行之改良式螢火蟲演算法及其於模糊控制器之設計。良好的最佳化方法必須於探索與搜索之間取的一個良好的平衡，本論文修改標準螢火蟲演算法提出了「標準模式」改善搜索的效能，為了加強探索的能力修改萊維飛行螢火蟲演算法提出了「萊維模式」，透過模式切換的方式結合兩種行為模式的優點。本論文將所提出的改良式螢火蟲演算法，以常用的23個最佳化基準測試函數，進行模擬實驗，其中包含單峰值與多峰值測試函數，經過性能測試，從模擬實驗結果顯示改良式螢火蟲演算法與其他螢火蟲演算法比較，得到最佳解的次數較多且收斂性亦較佳。本論文再利用改良式螢火蟲演算法於非線性系統的模糊控制器參數最佳化，設計模糊控制器內所需之參數調整，使其參數最佳化，經由模擬結果證明提出方法之有效性與可行性。

This thesis proposes an improved firefly algorithm that applies Lévy flight to standard firefly algorithm to achieve better performance in resolving global optimization problems. The application of designing fuzzy controllers is also examined. A good optimization method needs to have a well-balanced tradeoff between exploration and exploitation. We present the ‘‘Standard Mode’’ by modifying standard firefly algorithm to improve the effort of exploitation. In order to enhance the ability of exploration, we modify the Lévy-flight firefly algorithm and propose the ‘‘Lévy Mode’’. We combine the advantages of exploration and exploitation by mode switch between ‘Standard Mode’ and ‘‘Lévy Mode’’. The proposed improved firefly algorithm is successfully applied to resolve 23 benchmark problems of global numerical optimization, where both unimodal and multimodal functions are included. The simulation results show that the improved firefly algorithm in comparison with the others offers more opportunities to find the optimal solutions and has also superior performance in convergence rate. Finally, this study aims to figure out all the parameters of the fuzzy logic controller for four nonlinear systems. All the simulation results demonstrate the effectiveness and feasibility of the proposed schemes.

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