結合粒子群與螢火蟲演算法於結構最佳化設計之研究

Optimum Design of Structures by A Hybrid Firefly and Particle Swarm Optimization Algorithm

徐祥恩(Hsiang-En Hsu)

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(2018年)

https://doi.org/10.6846/TKU.2018.00336

摘要

本論文應用粒子群螢火蟲演算法於結構最佳化設計中。粒子群演算法為仿生演算法，其特點為收斂速度快，參數設定少、搜尋範圍廣泛及具有記憶性。螢火蟲演算則是模擬螢火蟲在求偶時使用亮光互相吸引，在空間中尋找最亮光源的特性來尋找問題的最佳解。螢火蟲演算法將區域中的各初始值模擬為各個螢火蟲個體，賦予各個體初始吸引值並依照光衰公式定義出吸引力關係式而推導出迭代關係式，使各值趨近於最佳解。粒子群螢火蟲演算法則是結合粒子群演算法和螢火蟲演算法兩種方法進行運算，利用螢火蟲演算法之光吸收強度特性增加區域搜尋效率，並透過粒子群演算法多點搜尋的能力加快收斂之速度。本研究結合兩種演算法的優點成一混合之演算法，以達到運算之效益並減少運算時間。數值範例中將對各種結構做分析與討論，結果顯示粒子群螢火蟲演算法能夠在結構最佳化取得不錯的成效。

關鍵字

粒子群演算法 ； 螢火蟲演算法 ； 最佳化設計

並列摘要

Optimum design of structure by a hybrid firefly and particle swarm algorithm is used in this study. Particle Swarm Optimization(PSO) algorithm is a bionic technique which has fast convergence, less parametric setting and wide search range with memory.
Firefly algorithm is conceptualized fireflies being attracted to each other by flashing light when mating season, and searching the brightest one in space area. The advantages of the firefly algorithm are fewer parameters need to be adjusted and the iterations converge efficiently. Firefly algorithm using inverse-square law to determined iterative function and definition of the attractiveness. In this study, a Hybrid Firefly and Particle Swarm Optimization algorithm is proposed for structural optimal design. In the Hybrid method, through the modified light intensity attraction characteristic of Firefly algorithm to improve local search ability and integrated with the global search technique of PSO algorithm can enhance the searching strategy. Optimum design of different structures were analyzed and discussed in Numerical Examples. The results of numerical examples showed that the optimum design of structures are better than other references in this study.

並列關鍵字

Particle Swarm Algorithm ； Firefly Algorithm ； Optimum Design

參考文獻

參考文獻

[1]林柏勳、胡光復、沈哲偉、辜炳寰、鄭錦桐，「最佳化方法於工程上之運用」，中興工程期刊第103期，pp.13-24，2009年。

[2]Yang,X.S., “Firefly Algorithm,” Nature-Inspired Optimization Algorithms, pp.111-127, 2014.

[3]Yang,X.S., “Firefly Algorithm, Lévy Flights and Global Optimization”, Research and Development in Intelligent Systems XXVI(Eds,M.Bramer, R.Ellis,M.Petridis), Springer London, pp.209-218, 2010.

[4]Gandomi,A.H., Yang ,X.S. and Alavi,A.H., “Mixed Variable Structural Optimization Using Firefly Algorithm” ,Computers & Structures, Vol. 56,pp.23-37, 2013.