MapReduce-Based Ant Colony Optimization Algorithm for Multi-Dimensional Knapsack Problem
2013 ◽
Vol 380-384
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pp. 1877-1880
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Keyword(s):
This paper uses MapReduce parallel programming mode to make the Ant Colony Optimization (ACO) algorithm parallel and bring forward the MapReduce-based improved ACO for Multi-dimensional Knapsack Problem (MKP). A variety of techniques, such as change the probability calculation of the timing, roulette, crossover and mutation, are applied for improving the drawback of the ACO and complexity of the algorithm is greatly reduced. It is applied to distributed parallel as to solve the large-scale MKP in cloud computing. Simulation experimental results show that the algorithm can improve the defects of long search time for ant colony algorithm and the processing power for large-scale problems.
2014 ◽
Vol 548-549
◽
pp. 1213-1216
2012 ◽
Vol 433-440
◽
pp. 3577-3583
2014 ◽
Vol 8
(1)
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pp. 96-100
2017 ◽
Vol 2017
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pp. 1-15
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2014 ◽
Vol 614
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pp. 199-202
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2011 ◽
Vol 230-232
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pp. 973-977
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