Some Aspects of Ant Colony Algorithm
碩士 === 國立高雄海洋科技大學 === 輪機工程研究所 === 96 === In this study, a new pheromone reset method for ant colony algorithm to avoid local minima property is investigated. For validation, the comparison between the presented technique and Dorigo’s ant colony optimization algorithm for traveling salesman problems...
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ndltd-TW-096NKIM84840142015-10-13T14:52:53Z http://ndltd.ncl.edu.tw/handle/85269700296362976819 Some Aspects of Ant Colony Algorithm 對於螞蟻族群最佳化演算法的一些論點 Yu-Jie Zheng 鄭宇捷 碩士 國立高雄海洋科技大學 輪機工程研究所 96 In this study, a new pheromone reset method for ant colony algorithm to avoid local minima property is investigated. For validation, the comparison between the presented technique and Dorigo’s ant colony optimization algorithm for traveling salesman problems are conduced. Ant Colony System (ACS) is a recently proposed meta-heuristic approach for solving combinatorial optimization problems. In order to get the best optimal solution, the ants in the ACS not only select the route known, but also the route which never tracked. Exploitation and Exploration are the two mechanism for selecting the route. The huge combinatorial optimization problems that the pheromone evaporation rapidly by using traditional ACS algorithm. This study is proposed to add the artificial reset pheromone to improvement solution effect. While the ants are convergence, the pheromone on the route reset and the ants search the solution again. This study provided experiment with some examples form TSPLIB. Adding the artificial reset pheromone can promote the accuracy to well Ker-Wei Yu 俞克維 2008 學位論文 ; thesis 38 zh-TW |
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碩士 === 國立高雄海洋科技大學 === 輪機工程研究所 === 96 === In this study, a new pheromone reset method for ant colony algorithm to avoid local minima property is investigated. For validation, the comparison between the presented technique and Dorigo’s ant colony optimization algorithm for traveling salesman problems are conduced.
Ant Colony System (ACS) is a recently proposed meta-heuristic approach for solving combinatorial optimization problems. In order to get the best optimal solution, the ants in the ACS not only select the route known, but also the route which never tracked. Exploitation and Exploration are the two mechanism for selecting the route.
The huge combinatorial optimization problems that the pheromone evaporation rapidly by using traditional ACS algorithm. This study is proposed to add the artificial reset pheromone to improvement solution effect. While the ants are convergence, the pheromone on the route reset and the ants search the solution again. This study provided experiment with some examples form TSPLIB. Adding the artificial reset pheromone can promote the accuracy to well
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Ker-Wei Yu |
author_facet |
Ker-Wei Yu Yu-Jie Zheng 鄭宇捷 |
author |
Yu-Jie Zheng 鄭宇捷 |
spellingShingle |
Yu-Jie Zheng 鄭宇捷 Some Aspects of Ant Colony Algorithm |
author_sort |
Yu-Jie Zheng |
title |
Some Aspects of Ant Colony Algorithm |
title_short |
Some Aspects of Ant Colony Algorithm |
title_full |
Some Aspects of Ant Colony Algorithm |
title_fullStr |
Some Aspects of Ant Colony Algorithm |
title_full_unstemmed |
Some Aspects of Ant Colony Algorithm |
title_sort |
some aspects of ant colony algorithm |
publishDate |
2008 |
url |
http://ndltd.ncl.edu.tw/handle/85269700296362976819 |
work_keys_str_mv |
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