scholarly journals Preference Incorporation into Many-Objective Optimization: An Ant Colony Algorithm based on Interval Outranking

2021 ◽  
pp. 101024
Author(s):  
Gilberto RIVERA ◽  
Carlos A. COELLO COELLO ◽  
Laura CRUZ-REYES ◽  
Eduardo R. FERNANDEZ ◽  
Claudia GOMEZ-SANTILLAN ◽  
...  
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony

Data association method of SLAM based on ant colony algorithm

2009 ◽  
Vol 29 (1) ◽  
pp. 136-138 ◽  
Author(s):  
Wen-jing ZENG ◽  
Tie-dong ZHANG ◽  
Yu-ru XU ◽  
Da-peng JIANG
Keyword(s):  
Ant Colony Algorithm ◽  
Data Association ◽  
Ant Colony ◽  
Association Method

Improvement of PageRank algorithm by ant colony algorithm

2009 ◽  
Vol 29 (10) ◽  
pp. 2726-2728
Author(s):  
Yue-wei DING ◽  
Hui GUO
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Pagerank Algorithm

Cross-country path planning based on improved ant colony algorithm

2013 ◽  
Vol 33 (4) ◽  
pp. 1157-1160
Author(s):  
Tianyi WU ◽  
Jiheng XU ◽  
Jianyong LIU
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Cross Country ◽  
Improved Ant Colony Algorithm

Cloud computing demand elasticity algorithm based on ant colony algorithm

2020 ◽  
Vol 13 ◽  
Author(s):  
Chunyu Liu ◽  
Fengrui Mu ◽  
Weilong Zhang
Keyword(s):  
Ant Colony Algorithm ◽  
Theory Of Elasticity ◽  
Ant Colony ◽  
Demand Elasticity ◽  
Network Efficiency ◽  
Elasticity Of Demand ◽  
Operating Profit ◽  
Network Load ◽  
Network Utilization ◽  
The Impact

Background: In recent era of technology, the traditional Ant Colony Algorithm (ACO) is insufficient in solving the problem of network congestion and load balance, and network utilization. Methods: This paper proposes an improved ant colony algorithm, which considers the price factor based on the theory of elasticity of demand. The price factor is denominated in the impact on the network load which means indirect control of network load, congestion or auxiliary solution to calculate the idle resources caused by the low network utilization and reduced profits. Results: Experimental results show that the improved algorithm can balance the overall network load, extend the life of path by nearly 3 hours, greatly reduce the risk of network paralysis, and increase the profit of the manufacturer by 300 million Yuan. Conclusion: Furthermore, results shows that the improved method has a great application value in improving the network efficiency, balancing network load, prolonging network life and increasing network operating profit.

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