海底油管是進口原油從外海卸油站運輸至岸邊油槽的主要幹道。本研究基於粒子群演算法(PSO)與自我學習粒子群演算法(SLPSO)，探討在地形變化、油管流量及油管長度等因素下對海底油管路徑做最佳化設置規劃。
粒子群演算法是解決全域最佳化最有效率的工具之一。在文獻顯示中粒子群算法的粒子在做下一步選擇時皆使用相同的策略，意即僅使用一種學習模式。在這種情況下因為社會及認知模式牽制的關系會使得粒子容易陷入局部最佳解的問題而無法處理太過於複雜的問題。為了解決此問題，本研究應用了自我學習演算法的方法。在此演算法中，為因應不同的環境而將學習策略分為四種，使得每個粒子可以依據自身不同的適應值而做相對應的學習策略選擇
一般而言，最佳路徑規劃在工程應用上最佳解表示為最短路徑，換言之，即為所需費用最低的路徑。然而在實際工程應用上，需考量到不同地形狀況而所需的工程難度及費用亦會有所不同。在本文研究中，基於這個出發點來考慮海底油管路徑規劃，最短路徑並非代表費用最低的路徑。本論文應用自我學習粒子群演算法於不同施工費用權重下來解決海底油管路徑設置規劃的問題。由本研究的模擬結果顯示出，自我學習粒子群演算法相較粒子群演算法能更有效率且更能準確地找到最佳解。

Submarine pipeline is the main access route for transporting imported crude oil from the offshore oil dump station to the oil tank near the shore. In this study, the particle swarm optimization (PSO) and the self-learning particle swarm optimization (SLPSO) were used to obtain the optimal submarine pipeline layout planning considering the facts including the changes in the subsea terrain, pipeline flow, and pipeline lengths.

PSO is one of the most efficient tools for solving global optimization. Literatures have shown that when particles in the PSO make a subsequent selection, the same strategy is used. That is, only one learning model is used. Under this situation, due to social and cognitive model constraints, particles are likely to fall into local optimal solutions and thus be unable to process excessively complex problems. In order to resolve this problem, the self-learning algorithm was applied in this study. This algorithm divides learning strategies into four types to adapt to different environments, thus enabling each particle to choose a corresponding learning strategy based on their varied adaptation values.

In general, the best solution for engineering applications related to optimum path planning, i.e., the shortest path. In other words, it refers to the path requiring the lowest cost. However, in actual engineering applications, the degree of engineering difficulty and costs that arise based on different terrain conditions should be taken into consideration. Based on the viewpoint in this study, the shortest path does not represent the path with the lowest costs. Hence, the shortest path does not necessarily represent the path with the lowest cost. The SLPSO with different construction cost weights was applied in this thesis to solve submarine pipeline layout planning. The simulation results in this study show that the SLPSO can derive the optimal solution more efficiently and more accurately compared to the PSO.

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