應用粒子群鯨魚演算法於結構最佳化設計之研究

Optimum Design of Structures by A hybrid Particle Swarm and Whale Optimization Algorithm

廖謙文(Chien-Wen Liao)

指導教授 ： 張永康

淡江大學/工學院/航空太空工程學系碩士班/碩士(2019年)

https://doi.org/10.6846/TKU.2019.00759

摘要

本論文結合粒子群演算法與鯨魚演算法於結構最佳化設計中。粒子群演算法是一種模擬鳥類覓食行為的仿生演算法，其特點為架構簡單、設計參數少且收斂速度快。鯨魚演算法是一種模擬座頭鯨泡泡網獵食行為的演算法，鯨魚演算法有三種搜索模式能夠避免落入區域解並增加大範圍的搜索機率。在搜索階段分為兩種：探勘階段與開發階段，其中在開發階段又分為螺旋更新位置與收縮環繞機制。粒子群鯨魚演算法則是結合粒子群演算法與鯨魚演算法進行運算，利用鯨魚演算法特殊的更新位置方式搜索最佳解，減少計算時間。混合之演算法將鯨魚演算法得到之最佳解提供給粒子群演算法作為個體最佳值之參考，透過粒子群演算法的多點搜索能力加快收斂速度。本研究結合此兩種演算法的優點混合為一演算法，由數值分析範例結果顯示，應用粒子群鯨魚演算法於結構最佳化可取得不錯的效果。

關鍵字

粒子群演算法 ； 鯨魚演算法 ； 最佳化設計

並列摘要

Optimum design of structures by a hybrid Particle Swarm Optimization and Whale Optimization Algorithm are used in this study. The Particle Swarm Optimization(PSO) is inspired by simulation of social psychological expression of birds which has simple concept, less control parametric setting and fast convergence. The Whale Optimization Algorithm(WOA) is inspired from the bubble-net hunting behavior by humpback whales. There are three search methods to avoid falling into local solution and enhance the search probability of a global range. There are two kinds of search stage, which are exploration phase and exploitation phase. In the exploitation stage, we can use shrinking circle mechanism and spiral updating position to search the best value. PSO-WOA comprises of best characteristic of both Particle Swarm Optimization and Whale Optimization Algorithm. The hybrid algorithm treat the optimal value of the WOA as the best previous position of the Particle. Numerical examples were analyzed and discussed by the hybrid method, the results of numerical analysis showed that optimum design of structures by PSO-WOA are better than other references.

並列關鍵字

Particle Swarm Optimization ； Whale Optimization Algorithm ； Optimum Design

參考文獻

參考文獻

[1]郭信川、官佳慶，「隨機搜尋法於多極值最佳化問題之應用」，中國造船暨輪機工程師學刊，第十九卷，第33−40頁，2000年。

[2]Holland, J. H., “Adaptation in Natural and Artificial System,” University of Michigan Press, Ann Arbor, 1975.

[3]Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P., “Optimization by Simulated Annealing,” Science, Vol. 220, No. 4598, pp.671-680, 1983.

[4]Colorni, A., Dorigo, M., and Maniezzo, V., “Distributed optimization by ant colonies,” Proceedings of the 1st European Conference on Artificial Life, pp. 134-142, 1991.