太陽能光電模組受限於部分遮蔭的情況下，其輸出功率會大幅的降低，然而，有不少演算法就是為了能夠減緩部分遮蔭所帶來的負面影響。而在其中，啟發式演算法(Meta-huristic Algorithm)最為能夠有效的運用本身特點還能夠不被區域最佳解所侷限，進而找到最大功率點的位置所在，儘管因部分遮蔭所造成 P-V 曲線形狀的變異，還是有能力進行搜尋。
　　螢火蟲演算法(Firefly Algorithm)為近期才發展出來的一種啟發式演算法，在部分遮蔭的情況中，不僅追蹤的次數，效率也一樣優於常見的粒子群最佳化法，這是滿值得探討的議題之一。但是，螢火蟲演算法還是有一些影響到最大功率追蹤的隱憂潛藏在其中。不但每次的疊代運算都需要繁複的計算量，每隻螢火蟲也有可能因為受太多粒子的牽動，而作過多無謂的游移。因此，為了改善上述的兩個問題，本論文提出改良型的螢火蟲演算法(Improved Firefly Algorithm)就此衍生出來。新型的螢火蟲演算法為文章的主軸，主要是闡述利用此種演算法，既能減少原演算法的運算量，還能縮減收斂所需的時間。實驗結果顯示，此種新演算法在不同的遮蔭情形中，皆擁有更快的收斂速度，還能迅速的讓螢火蟲群聚於最大功率點附近進行追蹤的動作。

Photovoltaic (PV) modules subjected to partial shading conditions (PSC) can drastically decrease their power out. Hence, there have been various maximum power point tracking (MPPT) control algorithms developed to reduce or counteract the shading effects. Recently, a new meta-heuristic algorithm known as firefly algorithm (FA) was developed, which, under PSC, has been shown to successfully track the global maximum power point. Nevertheless, the FA still has some inherent problems, which may hinder the performance of the MPPT.
This paper improves the existing FA to counteract these problems. As will be demonstrated in the paper, the proposed improved FA (IFA) method can reduce the number of computation operations and the time for converging to the GMP that the existing FA requires. Experimental results show that the proposed method can track the global point under various PSCs with a faster convergence time, compared to some of the state-of-the-art, and can effectively suppress the power and voltage fluctuations.

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