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A fine-grained fast parallel genetic algorithm based on a ternary optical computer for solving traveling salesman problem

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Abstract

In this paper, we propose a fine-grained fast parallel genetic algorithm based on a ternary optical computer. Owing to the natural parallelizability of genetic algorithms, we take advantage of the characteristics of ternary optical computers, such as a large number of processor bits that can be reconfigured or allocated bit-by-bit, parallel computation, and low power consumption. The design and implementation of a parallel genetic algorithm on a ternary optical computer by solving the traveling salesman problem (TSP) is used as an example. Theoretical analysis and experimental results show that our proposed algorithm provides more efficient performance when the size of the members of the cities continuously increases according to the acceleration ratio measurement results. It also lays the theoretical foundation for the application of ternary optical computers in data-intensive computing.

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Acknowledgements

Thank Prof. Jin Yi, all Ph. D candidates and master graduate students of our research center for their kind help and valuable discussions in preparing the paper. We also thank Prof. Guan Dawen for his help with the english grammar in the revised paper.

Funding

This work is supported by National Natural Science Foundation of China (Grant No.61572305), Hainan Higher Education Reform Research Project of China (Grant No. Hnjg2017-46, No.Hnjg2022-90), Hainan Provincial Natural Science Foundation of China(Grant No.121RC1071, No.622MS084) and University-level general research project of Shanghai Normal University of China(Grant No.SK202121).

Author information

Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Zhehe Wang & Shuxin Wang

  2. School of Information Science and Technology, Sanda University, Shanghai, 201209, China

    Yunfu Shen

  3. College of Computer Science and Technology, Hainan Tropical Ocean University, Sanya, 572022, China

    Zhehe Wang

  4. College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, 200234, China

    Shuang Li

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  1. Zhehe Wang
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Correspondence to Yunfu Shen.

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Appendices

Appendix A: Abbreviations comparison table

Acronym

Full name

CPU

Central Processing Unit

CUDA

Compute Unified Device Architecture

FPGA

Field Programmable Gate Array

GA

Genetic Algorithm

GPU

Graphics Processing Unit

HPC

High Performance Computing

LCD

Liquid Crystal Display

MPI

Massage Passing Interface

MSD

Modied Signed Digital

NPC Problem

Non-deterministic Polynomial Complete Problem

PGA

Parallel Genetic Algorithm

SD16

Chinese spelling of Shanghai University-2016

TOC

Ternary Optical Computer

TOC-FFPGA

Fine-grained Fast PGA based on a TOC

TOP

Ternary Optical Processor

TSP

Traveling Salesman Problems

Appendix B: Variables or function comparison table

Variable/Function name

Meaning

\(Adder\_i\)

The i-th adder or arithmetic unit

BestFitness

Best Fitness

C(t)

Individual crossover function

ChrFit[PopSize]

Buffer for storing optimal chromosomes

ChrLength

The length of chromosome

CityChrNo[n][n]

Storing city information

\(Comparer\_i\)

The i-th comparator or logical unit

CurGen

Curent generation number

\(Fitness(chr_i)\)

Calculation function for the i-th individual fitness

M(t)

Individual mutation function

MaxGen

Maximum number of generations

NewCityChrNo[n][n]

Storing new city information

P(CurGen)

Population initialization function

\(P_c\)

Crossover probability

\(P_m\)

Mutation probability

PopSize

Population number

\(P_s\)

Selection probability

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Wang, Z., Shen, Y., Li, S. et al. A fine-grained fast parallel genetic algorithm based on a ternary optical computer for solving traveling salesman problem. J Supercomput 79, 4760–4790 (2023). https://doi.org/10.1007/s11227-022-04813-9

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