research-article

Solving traveling salesman problems via a parallel fully connected ising machine

Authors:

Jie HanAuthors Info & Claims

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

Pages 1123 - 1128

https://doi.org/10.1145/3489517.3530595

Published: 23 August 2022 Publication History

Abstract

Annealing-based Ising machines have shown promising results in solving combinatorial optimization problems. As a typical class of these problems, however, traveling salesman problems (TSPs) are very challenging to solve due to the constraints imposed on the solution. This article proposes a parallel annealing algorithm for a fully connected Ising machine that significantly improves the accuracy and performance in solving constrained combinatorial optimization problems such as the TSP. Unlike previous parallel annealing algorithms, this improved parallel annealing (IPA) algorithm efficiently solves TSPs using an exponential temperature function with a dynamic offset. Compared with digital annealing (DA) and momentum annealing (MA), the IPA reduces the run time by 44.4 times and 19.9 times for a 14-city TSP, respectively. Large scale TSPs can be more efficiently solved by taking a k-medoids clustering approach that decreases the average travel distance of a 22-city TSP by 51.8% compared with DA and by 42.0% compared with MA. This approach groups neighboring cities into clusters to form a reduced TSP, which is then solved in a hierarchical manner by using the IPA algorithm.

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https://doi.org/10.1587/transinf.2024PAP0007
Kuroki KJimbo SChu TMotomura MKawamura KLi XHandl J(2024)Classical Thermodynamics-based Parallel Annealing Algorithm for High-speed and Robust Combinatorial OptimizationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654042(196-205)Online publication date: 14-Jul-2024
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Huang ZZhang YWang XJiang DYao E(2024)DCAP: A Scalable Decoupled-Clustering Annealing Processor for Large-Scale Traveling Salesman ProblemsIEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2024.344969371:12(6349-6362)Online publication date: Dec-2024
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cover image ACM Conferences

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

July 2022

1462 pages

ISBN:9781450391429

DOI:10.1145/3489517

General Chair:
Rob Oshana
NXP

Copyright © 2022 ACM.

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Published: 23 August 2022

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Cited By

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https://doi.org/10.1587/transinf.2024PAP0007
Kuroki KJimbo SChu TMotomura MKawamura KLi XHandl J(2024)Classical Thermodynamics-based Parallel Annealing Algorithm for High-speed and Robust Combinatorial OptimizationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654042(196-205)Online publication date: 14-Jul-2024
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https://doi.org/10.1109/TCSI.2024.3449693
Kanai KKawahara T(2024)Efficient Implementation of Parallel Annealing Method with Heisenberg Model2024 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)10.1109/APCCAS62602.2024.10808590(55-59)Online publication date: 7-Nov-2024
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Tao QZhang THan J(2023)An Approximate Parallel Annealing Ising Machine for Solving Traveling Salesman ProblemsIEEE Embedded Systems Letters10.1109/LES.2023.329873915:4(226-229)Online publication date: 25-Sep-2023
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Inoue GOkonogi DVan Chu TYu JMotomura MKawamura K(2023)Flexibly Controllable Dynamic Cooling Methods for Solid-State Annealing Processors to Improve Combinatorial Optimization Performance2023 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS)10.1109/COOLCHIPS57690.2023.10121990(1-3)Online publication date: 19-Apr-2023
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