應用部門式基因演算法對離岸風機支撐結構最佳化

A department model genetic algorithm for offshore wind substructure optimization

劉定朋(Ding-Peng Liu)

指導教授 ： 黃心豪

國立臺灣大學/工學院/工程科學及海洋工程學研究所/碩士(2018年)

https://doi.org/10.6342/NTU201802270

摘要

根據設計規範，本研究建立極限風速的有限元素分析模型進行支撐結構最佳化設計，在考慮安全的限制條件的前提下，對支撐結構的厚度及半徑做最佳化設計，以降低支撐結構質量，減少離岸風電的建造成本。為了解決舊有的傳統型基因演算法計算效率不佳的問題，本研究在島嶼型的基因演算法加入決定性的最佳化方法─模型搜索法，並與粒子群算法同時尋値，將島嶼設計成各具不同功能的「部門」，繼而提出一種改良式基因演算法—部門式基因演算法。
經過演算法性能分析與13種改良式或混合演算法，以14個測試函式進行比較，證明部門式基因演算法具有高的效能及效率。為了驗證其實用性，將此方法應用至工程實例—離岸風機支撐結構最佳化設計的計算中，取得較傳統方法更佳的結果，且具有更高的效率，可節省電腦的運算成本。因此部門式基因演算法可作為設計離岸風機支撐結構較具效率並可靠的最佳化方法。

關鍵字

有限元素分析 ； 最佳化設計 ； 島嶼型基因演算法 ； 離岸風機支撐結構 ； 模型搜索法 ； 粒子群算法

並列摘要

Referring to the design standards, we constructed the finite element models for offshore wind substructure optimization under extreme conditions. The goal of the optimization was to reduce the weight of the substructure by regenerating the thickness and radius of the truss members. Conventionally, the Genetic Algorithm (GA) is notorious for high computational cost. The hybrid method combining the deterministic method and the GA was recognized as a feasible way for improving the efficiency. In this study, the Department Modeled Genetic Algorithm (DMGA) was proposed by adding the Pattern search (PS) and the Particle Swarm Optimization (PSO) methods into the Island Modeled Genetic Algorithm (IMGA) whereas each island possesses its own function and collaborates with other islands in the form of “departments”.
Great efficiency and effectiveness of the DMGA were proved by performance analysis. Totally 13 different modified or hybrid algorithms were tested through 14 test functions in comparison. For demonstrating the application of engineering problems, we further applied the DMGA for optimizing the wind turbine substructures and obtained an efficient design with lighter weight of the substructure. In summary, the DMGA was shown to be robust and efficient for optimizing the structural weight.

並列關鍵字

Finite element analysis ； Optimization ； Island modeled genetic algorithm ； Offshore wind turbine substructure ； Pattern search ； Particle swarm optimization.

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