DocumentCode :
2787532
Title :
Parallel implementation of ant colony optimization on MPP
Author :
Chen, Ling ; Sun, Hai-ying ; Wang, Shu
Volume :
2
fYear :
2008
fDate :
12-15 July 2008
Firstpage :
981
Lastpage :
986
Abstract :
An adaptive parallel ant colony algorithm (PACO) is presented. In the algorithm, we propose a strategy for information exchange between processors which make each processor choose its partner to communicate and update the pheromone adaptively. We also propose a method of adjusting the time interval of information exchange adaptively according to the diversity of the solutions so as to increase the ability of search and avoid early convergence. Experimental results show that our algorithm PACO has high convergence speed, high speedup and efficiency.
Keywords :
convergence; parallel algorithms; search problems; adaptive parallel ant colony optimization algorithm; adaptive pheromone update; convergence speed; information exchange time interval; massive parallel processors; processor communication; processor information exchange; search ability; traveling salesman problem; Ant colony optimization; Computer science; Convergence; Cybernetics; Fault detection; Frequency; Load management; Machine learning; Software algorithms; Traveling salesman problems; Ant colony optimization; Diversity; Parallel; Traveling Salesman Problem;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
Type :
conf
DOI :
10.1109/ICMLC.2008.4620547
Filename :
4620547
Link To Document :
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