近年來，在NP-hard 最佳化問題研究領域中，組合最佳化的問題受到越來越多的關注。對於較大的NP-hard 組合最佳化問題，貪婪或動態規劃等傳統算法不能在合理的時間內獲得理想的解決方案。許多專家與學者提出了許多啟發示演算法，用以解決如NP-hard 組合最佳化的問題，而粒子群聚演算法(PSO) 為最廣為人知的一種群體智慧演算法。然而，粒子群聚演算法(PSO) 由於收斂過早，尤其是在接近局部最佳化的時候，會導致群體多樣性迅速降低，為了改善此問題，本文提出了一種高效率的演算法，稱為混合式粒子群聚演算法(MPSO)。本文專注於三種類型的組合最佳化問題：群集、流線型工廠排程及DNA 片段重組問題。此外，另提出了一種混合式重力搜尋演算法(MGSA)，用以應用於解決群集、流線型工廠排程及DNA 片段重組問題。MGSA 是一種基於萬有引力的搜索演算法(GSA) 而形成的一個新的群體智慧演算法；而GSA 是基於牛頓重力學而衍生出的一種演算法，其搜尋空間是從透過過所有質點所獲得的全體力量而計算而來。經實驗結果，MGSA 可得到比MPSO 更好的結果，美中不足的是MGSA 所耗計算之時間比MPSO 更長。因此，為了結合PSO與GSA 兩者之優點，本文另將兩者結合，提出了一個新的混合式群體智慧演算法，稱為重力粒子群聚演算法(PSGO)。為了驗證PSGO，本文測試了5 個常用的函數優化問題，而經由實驗結果證實，PSGO 可以得到比PSO 或GSA 更好的結果。

Recently, combinatorial optimization problems have received increasing attention within
the NP-hard optimization research community. For large NP-hard combinatorial optimization
problems, traditional algorithms such as greedy or dynamic programming do not obtain
the ideal solution within a reasonable amount of time. Therefore, meta-heuristic approaches
have been proposed for NP-hard combinatorial optimization. Particle swarm optimization
(PSO) is the most well-known swarm-based intelligence algorithm. However, PSO converges
prematurely, which rapidly decreases the population diversity, especially when approaching
local optima. To improve the diversity of the PSO algorithm, in this study, the
authors proposed a new and efficient algorithm called memetic particle swarm optimization
(MPSO). The focus is on three types of optimization problems, clustering, flow-shop scheduling,
and DNA fragment assembly, have been focused on. In addition, to know the limitations
of solution quality of PSO, a memetic gravitation search algorithm (MGSA) has been proposed
in this thesis to solve the same problem as MPSO. GSA is a new swarm-based intelligence
algorithm that employs Newtonian gravity, and the search space is calculated from the
overall force obtained by all agents. Simulation results show that MGSA is more efficient
than MPSO, but its computation time is considerably more than that of MPSO. Therefore, to
exploit the advantages of both PSO and GSA algorithms, the fast convergence of PSO and
the high quality of GSA, PSO, and GSA have been combined in this work to generate a new
memetic algorithm called particle swarm gravitation optimization (PSGO). To verify PSGO,
we tested five well-known function optimization problems in this thesis, and the simulation
results showed that PSGO obtain get considerably better solutions than PSO or GSA.

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