本論文提出一個以混合田口粒子群交配演算法 (HTPC:Hybrid-Taguchi PSO with Crossover Algorithm)之模糊控制器設計。由於粒子群演算法容易陷入區域最佳解以及有收斂過早的趨勢，因此本論文利用田口方法並搭配交配運算子運用於粒子群演算法中，避免粒子過早收歛以及強化粒子跳脫局部最佳解的能力。首先，本論文將所提出的HTPC演算法對23種測試函數(包括單峰和多峰函數)進行性能檢測。從模擬測試結果顯示HTPC不僅能解決多峰函數問題在搜尋最佳解過程中容易陷入區域最佳解的問題，對於單峰函數問題亦能找到全域最佳解或接近全域最佳解的結果。同時將HTPC與其他演算法做比較，其結果顯示HTPC具有較佳的尋優能力以及較快的收歛速度。最後，運用HTPC於非線性系統的模糊控制器之最佳化設計，將控制器內欲調整之參數予以最佳化，經由模擬結果證明此方法之有效性與可行性。

In this study, a Hybrid Taguchi Particle swarm optimization with Crossover (HTPC) is proposed for designing a fuzzy controller. Particle swarm optimization (PSO) easily falls into the trap of a locally optimal solution and has premature convergence shortcomings. We use the Taguchi method with crossover operator to assist in PSO to avoid the premature convergence and escape the local optimum. The proposed HTPC is effectively applied to solving 23 benchmark problems of global numerical optimization (which have unimodal and multimodal functions). The simulation experiments show that the proposed HTPC not only can solve problems that easily fall into the trap of locally optimal solutions during optimization for multimodal optimization problems, but also can find the optimal solutions or close to the optimal solutions for unimodal optimization problems. Simultaneously, HTPC is compared with other algorithms. The results show that HTPC has a superior performance in convergence rate than other algorithms. Finally, we use HTPC to tune the scaling factors of the fuzzy controller for nonlinear systems. The simulation results demonstrate the validity and feasibility of the proposed methodology.

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