粒子數目的設定

關於粒子的數目與收斂速度，由於收斂的速度與初始的粒子分佈有很大的 關係，如果一開始就有粒子很靠近最佳解位置，很快的，粒子就會收斂到最佳 解位置附近。如果粒子都距離最佳解位置很遠，則需要多次的演化之後，粒子 才有可能到達最佳解的位置。由於我們不知道最佳解的範圍，所以隨機分佈粒 子在空間中，由機率上來說，粒子的數目越多，一開始就有粒子很靠近最佳解 位置的機率也越高。但是由於粒子數目多，計算量也就越大，程式執行時間也 會增加。因此如何在粒子數目和收斂速度兩者間找到平衡點是未來研究的課題。

6.4 學習參數的設定

關於學習參數的設定，α為 gBest 方向的學習參數，

β

α

β

然後隨著演化次數漸漸變小。相反的在一開始將β設定較小，然後隨著演化次 數漸漸變大。這樣使得粒子在演化初期 gBest 方向的變動量大，達到廣域搜尋 的目的；在演化後期，粒子都相當靠近時，pBest 影響力變大，能有細部搜尋的 能力，在未來的研究中可加入此一考慮因素。

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