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Probabilistic greedy algorithm solver using magnetic tunneling junctions for traveling salesman problem.

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This study introduces a novel probabilistic framework using hardware true random number generators to enhance combinatorial optimization. The approach improves solution quality and convergence speed for complex problems like the traveling salesman problem.

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Area of Science:

  • Physics and Computer Science
  • Hardware-accelerated optimization algorithms

Background:

  • Classical combinatorial optimization methods face scalability challenges.
  • Balancing efficiency and solution quality remains a key issue in large-scale optimization.

Purpose of the Study:

  • To develop a probabilistic framework for combinatorial optimization using hardware true random number generators.
  • To enhance greedy search algorithms with controlled stochasticity for improved performance.

Main Methods:

  • Embedding spin-transfer-torque magnetic tunnel junction (STT-MTJ) based true random number generators (TRNGs) into a greedy solver.
  • Utilizing a temperature parameter to control randomness and balance exploration-exploitation.

Main Results:

  • The framework significantly improved solution quality and convergence speed on the traveling salesman problem compared to simulated annealing and genetic algorithms.
  • Near-optimal solutions were achieved with fewer iterations and reduced computational cost on larger problem instances (up to 70 cities).

Conclusions:

  • Hardware true randomness with tunable statistics offers a powerful method to enhance heuristic search algorithms.
  • Integrated, energy-efficient probabilistic hardware can enable scalable optimization solutions.