Improving ACO Convergence with Parallel Tempering

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10449)


Parallel Tempering (PT) is an efficient Monte Carlo simulation method known from statistical physics. We present a novel PT-based Ant Colony Optimization algorithm (PTACO) in which multiple replicas of the Ant Colony System enhanced with a temperature parameter (ACST) are executed in parallel. Based on computational experiments on a set of TSP and ATSP instances we show that the PTACO converges (in terms of solutions quality) significantly faster than the ACS and is competitive to the state-of-the-art Ant Colony Extended algorithm.


Ant Colony System Parallel Tempering Simulated Annealing Travelling salesman problem 



This research was supported in part by PL-Grid Infrastructure.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Computer ScienceSilesia UniversitySosnowiecPoland

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