Parallelizing Simulated Annealing Algorithm in Many Integrated Core Architecture

Junhao Zhou, Hong Xiao, Hao Wang*, Hong-Ning Dai

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

2 Citations (Scopus)


The simulated annealing algorithm (SAA) is a wellestablished approach to the approximate solution of combinatorial optimisation problems. SAA allows for occasional uphill moves in an attempt to reduce the probability of becoming stuck in a poor but locally optimal solution. Previous work showed that SAA can find better solutions, but it takes much longer time. In this paper, in order to harness the power of the very recent hybrid Many Integrated Core Architecture (MIC), we propose a new parallel simulated annealing algorithm customised for MIC. Our experiments with the Travelling Salesman Problem (TSP) show that our parallel SAA gains significant speedup.

Original languageEnglish
Title of host publicationComputational Science and Its Applications - 16th International Conference, ICCSA 2016, Proceedings, Part II
EditorsOsvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Ana Maria A.C. Rocha, Carmelo M. Torre, David Taniar, Bernady O. Apduhan, Elena Stankova, Shangguang Wang
Place of PublicationCham
Number of pages12
ISBN (Electronic)9783319421087
ISBN (Print)9783319421070
Publication statusPublished - 4 Jul 2016
Event16th International Conference on Computational Science and Its Applications, ICCSA 2016 - Beijing, China
Duration: 4 Jul 20167 Jul 2016 (Conference proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameTheoretical Computer Science and General Issues
ISSN (Print)2512-2010
ISSN (Electronic)2512-2029


Conference16th International Conference on Computational Science and Its Applications, ICCSA 2016
Internet address

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Parallel computing
  • Simulated annealing
  • MIC optimization


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