就粒子群優化法(PSO)而言，理想之參數控制應當考慮如何衡量整體粒子群及個別粒子之演化現況，進行偵測並提供回饋做為有效搜尋及演化之依據。有鑒於此，本研究的主要目的在於提出新的衡量指標，以偵測PSO的演化狀態，引入擾動式之慣性權重及加速度係數，以期能有效調控PSO探索搜尋(exploration)及探勘搜尋(exploitation) 之間的變動，藉以增強粒子群優化法之自適應性。
本研究提出兩個新的PSO策略，分別成為稱為PSO-LGR和PSO-FWAC。PSO-LGR作法是：偵測個別粒子與群體目前最佳解間之「位置」的偏離，集成導出慣性權重的調控係數，據以產生適性的演化策略，達成有效的演化。PSO-FWAC，則是根據個別粒子與群體目前最佳解間之「適應值」偏離，集成導出加速係數的調控權重，據以產生適性的演化策略，達成有效的演化。
經實驗驗證以PSO-LGR、PSO-FWAC求最佳解在準確率、成功率及收斂速度上均獲得顯著的改善。本研究證實PSO-LGR、PSO-FWAC能有效度量PSO的演化狀態，提供回饋，有效地控制探索與探勘搜尋之間的轉換，達到增強PSO的目的。本研究主要的貢獻在於：能根據粒子群最佳化法演化的現況，引入有效的擾動，以增強傳統粒子群最佳化法。

In spite of the varying position and fitness of each distinct particle, most of the PSO algorithms treat the given swarm of particles simply. This study aims to find good controls for facilitating exploration and exploitation movements to enhance the traditional particle swarm optimization (PSO) algorithm. In this sense, this study seeks improvements to PSO by introducing adaptive controls on inertia weight and acceleration coefficients according to their corresponding evolutionary states.
Two novel PSO strategies are proposed to facilitate the transitions between searches of exploration and exploitation on the corresponding evolutionary status instead of merely on time (number of iteration). The enhanced particle swarm optimization algorithms, referred as PSO-LGR (location gain regulator) and PSO-FWAC (fitness weighted acceleration coefficients), detect the evolutionary state based on the location and fitness of particles respectively.
Experimental results on widely used benchmark functions show that PSO-LGR and PSO-FWAC outperform the static and time-varying approaches in terms of the precision, success rate, and convergence speed of particle swarm optimization. It is considered as valuable contributions of this study that the proposed regulators are able to enhance traditional PSO by introducing appropriate turbulence depending on corresponding evolutionary states.

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