蟻群最佳化應用於結構拓樸最佳化

本文藉由螞蟻之間相互合作和殘留費洛蒙的特性衍生出一個適合結構拓樸最佳化的螞蟻最佳化(ACO)，能快速收歛是其演算法之優點。而拓樸結構最佳化設計的優點則是能擺脫設計者主觀的束縛，在目標函數值設定的限制條件下，往往能搜尋出一反設計者思維的結果。以往蟻群最佳化常常使用在解決TSP、QAP、VRP等問題上，而使用在解決結構拓樸最佳化問題上的例子極少，本文正是利用蟻群最佳化來進行結構拓樸最佳化設計，主要是將拓樸結構上的網格當作是螞蟻搜尋食物的路徑，並依據路徑上費洛蒙的高低當作選取的機率，當搜尋完一個完整路徑後，進入商業軟體(ANSYS)進行分析並產生所需資料，藉由這些輸出的資料可以計算此完整路徑的目標函數值當做此路徑上螞蟻所殘留的費洛蒙值。本文最後成功利用多種螞蟻搜尋食物的機制來整合蟻群最佳化和結構拓樸最佳化，並且有效的搜尋出符合設計限制條件的設計結構，證明蟻群最佳化可以搜尋出最佳解。

關鍵字

拓樸最佳化；蟻群最佳化

並列摘要

The ant algorithm has been applied to solve the TSP, QAP, and VRP and there are only a few papers using it to solving problem of topology optimization. This study combines the topology optimization of structure with an ant algorithm that derives from specific pheromone and cooperation mechanism between ants. The best advantage of the ant algorithm is rapid convergence while the benefit of topology optimization can get rid of the subjective ideas of designers and provides them with unexpected results .The contribution of this paper is to integrate ant algorithm with commercial software ANSYS for finding the best results for topology optimization of structure. A mesh topology of finite element model of structure was used as possible paths that ants find foods from. Every element of the model was treated as a node on the path for ant path. Then, the amount of accumulated pheromone deposited on every element(node) by different ants can be used to determine the probability of selection path for food-finding ants. After an ant completing a tour, the complete path was converted into a structure model and the software ANSYS was applied to analyze the structure. The output data was used to calculate the value of objective function that can be utilized as the amount of pheromone on each route trod by ants. From the results of studies in this paper, ant algorithm provides an alternate optimization method that has high potential in finding the best design for topology optimization of structure successfully and efficiently.

並列關鍵字

Topology Optimization ； Ant Colony Optimization

參考文獻

[1] D. E. Goldberg, “Genetic Algorithm in Search, Optimization, and Machine Learning”, Addison Wesley 1989

[5] M. Dorigo and L. M. Gambardella, “Ant Colony System : A cooperative learning approach to the traveling salesman problem” IEEE Transactions on Evolutionary Computation, 1(1) : 53-66, 1997.

[9] Zhou Pin, Li Xiao-ping, Zhang Hong-fang,”An Ant Colony Algorithm for Job Shop Scheduling Problem“, Proceedings of the 5th Congress on Intelligent Control and Automation , June 15-19, 2004, Hanghou,P.R. China .

[11] S.-C. Chu, C.-S. Shieh, and J. F. Roddick, “A tutorial on meta-heuristics for optimization,”in J.-S. Pan, H.-C. Huang, and L. C. Jain (Eds.), Intelligent Watermarking Techniques, World Scientific Publishing Company, Singapore, Chapter 4, pp. 97-132, 2004.

[12] S. Zheng, G. Zhang and Z. Zhou, “Ant Colony Optimization based on Pheromone Trail Centralization” IEEE, Proceedings of the 6th World Congress on Intelligent Control and Automation, Vol 3349-3352, June 21-23, 2006, Dalian, China.

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蟻群最佳化應用於結構拓樸最佳化

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