The Application of Ant Colony Optimization on the Optimal Design of Sewer Network System
碩士 === 立德管理學院 === 資源環境研究所 === 94 === The optimization method has been developed from traditional linear and non-linear programming to evolutionary algorithms in recent years, for example genetic algorithms (GAs), simulated annealing (SA), tabu search (TS) and the newest developed ant colony optimiza...
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ndltd-TW-094LU0057590292016-05-18T04:12:33Z http://ndltd.ncl.edu.tw/handle/26466217500328015837 The Application of Ant Colony Optimization on the Optimal Design of Sewer Network System 蟻行演算法應用於污水下水道管網系統最佳化設計 Wen-Hsiang Lin 林文祥 碩士 立德管理學院 資源環境研究所 94 The optimization method has been developed from traditional linear and non-linear programming to evolutionary algorithms in recent years, for example genetic algorithms (GAs), simulated annealing (SA), tabu search (TS) and the newest developed ant colony optimization (ACO). ACO has impressive achievements in making the application of combinatorial optimization problems. However, it hasn’t been applied to optimization of sewer network system so far. This research uses ACO to optimize the sewer network system. The excellent behavior of searching solved problems could let us have more chances to find optimal solution and test the important control parameters in order to provide a sound basis for parameter settings. The simulation result shows that the optimal design basis on the best parameters, under design criteria and assumed conditions, the saving by using small-scale sewer network system is not apparent. However, the cost of using medium-scale sewer network system saves 5.6% under minimum cost and original design plan. It is helpful to design sewer network system and economize on budgets. As a result, the outstanding achievements of ACO applies to optimize sewer network system has been proved. Chun-Sheng Wu 吳春生 2006 學位論文 ; thesis 65 zh-TW |
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碩士 === 立德管理學院 === 資源環境研究所 === 94 === The optimization method has been developed from traditional linear and non-linear programming to evolutionary algorithms in recent years, for example genetic algorithms (GAs), simulated annealing (SA), tabu search (TS) and the newest developed ant colony optimization (ACO). ACO has impressive achievements in making the application of combinatorial optimization problems. However, it hasn’t been applied to optimization of sewer network system so far. This research uses ACO to optimize the sewer network system. The excellent behavior of searching solved problems could let us have more chances to find optimal solution and test the important control parameters in order to provide a sound basis for parameter settings.
The simulation result shows that the optimal design basis on the best parameters, under design criteria and assumed conditions, the saving by using small-scale sewer network system is not apparent. However, the cost of using medium-scale sewer network system saves 5.6% under minimum cost and original design plan. It is helpful to design sewer network system and economize on budgets. As a result, the outstanding achievements of ACO applies to optimize sewer network system has been proved.
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author2 |
Chun-Sheng Wu |
author_facet |
Chun-Sheng Wu Wen-Hsiang Lin 林文祥 |
author |
Wen-Hsiang Lin 林文祥 |
spellingShingle |
Wen-Hsiang Lin 林文祥 The Application of Ant Colony Optimization on the Optimal Design of Sewer Network System |
author_sort |
Wen-Hsiang Lin |
title |
The Application of Ant Colony Optimization on the Optimal Design of Sewer Network System |
title_short |
The Application of Ant Colony Optimization on the Optimal Design of Sewer Network System |
title_full |
The Application of Ant Colony Optimization on the Optimal Design of Sewer Network System |
title_fullStr |
The Application of Ant Colony Optimization on the Optimal Design of Sewer Network System |
title_full_unstemmed |
The Application of Ant Colony Optimization on the Optimal Design of Sewer Network System |
title_sort |
application of ant colony optimization on the optimal design of sewer network system |
publishDate |
2006 |
url |
http://ndltd.ncl.edu.tw/handle/26466217500328015837 |
work_keys_str_mv |
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