本論文提出一種改良式的粒子群最佳化(Particle Swarm Optimization，PSO)演算法，本文稱之為具隨機粒子與微調機制式粒子群演算法(Particle Swarm Optimization with Random Particles and Fine-Tuning Mechanism，PSO-RPFT)，將隨機粒子(Random Particles，RP)及微調(Fine-Tuning，FT)兩個運算子導入PSO演算法中。前者可避免族群搜尋過程中落入局部最佳解，後者可提高演算法於最佳區域局部搜尋的能力，改善PSO於搜尋末期，粒子相似度過高的缺陷。本文設立了一套隨機粒子及微調運算子的完整建構。最後以7種不同複雜程度之多極值函數為範例，比較PSO-RPFT與廣受大家採用的PSO-CF兩演算法的最佳化能力。結果顯示，PSO-RPFT在搜尋成功率及平均收歛世代數的性能表現皆優於PSO-CF演算法。

This thesis aims at developing a modified particle swarm optimization (PSO) algorithm. The proposed method called PSO-RPFT will introduce two operators, “Random Particles” and “Fine-Tuning”, into the PSO algorithm. The former can prevent the population from trapping into the local optimum and the latter can promote the ability of local search to modify the defects of high similarity of individual particles on the late period of search following PSO algorithm. The complete architecture of random particles and fine-tuning operators is established in this thesis. At last the performance of PSO-RPFT and PSO-CF which was used widely in this filed will be compared by optimizing seven massively multimodal functions with varying complexities. The results show that the performance of PSO-RPFT is better than PSO-CF on both the search success rate and the average convergence generations.

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