RMACO :a randomly matched parallel ant colony optimization

Ant Colony Optimization (ACO), inspired by the foraging behavior of real ants, is a widely applied bionic algorithm. Driven by the requirements of applications and the advances of computing technologies, ACO has been studied extensively, and the parallelism of ACO becomes an important research area. In this paper, we analyze the key factors that affect the performance of parallel ACO, based on which we propose a randomly matched parallel ant colony optimization (RMACO) using MPI. In RMACO, we design a new interconnection communication topology based on which the processors communicate with each other using a randomly matched method, and propose a non-fixed exchange cycle as well. All of these ensure the quality of the solution found by ACO and reduce the execution time. The experimental results show that RMACO has better efficiency compared with existing typical parallel ACO approaches.

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