A Modified Ant Colony Algorithm for Traveling Salesman Problem

Xianmin Wei, Liqi Han, Lu Hong

Abstract


Ant colony algorithms such as Ant Colony Optimization (ACO) have
been effectively applied to solve the Traveling Salesman problem (TSP). However,
traditional ACO algorithm has some issues such as long iterative length and prone to
local convergence. To this end, we propose we embed ACO into Cultural Algorithm
(CA) framework by leveraging the dual inheritance mechanism. Best solutions are
evolved in both population space and belief space, and the communication between
them is achieved by accept and influence operations. Besides, we employ multiple
population spaces for parallel execution. Experiments show that the performance of
our proposed algorithm is greatly improved.


Keywords


Traveling Salesman Problem (TSP), Ant Colony Optimization (ACO), Cultural Algorithm (CA)

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References


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DOI: https://doi.org/10.15837/ijccc.2014.5.1280



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