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List-Based Simulated Annealing Algorithm for Traveling Salesman Problem.

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Area of Science:

  • Artificial Intelligence
  • Operations Research
  • Computer Science

Background:

  • Simulated annealing (SA) is a widely used optimization algorithm.
  • Parameter setting significantly impacts SA algorithm performance but is often complex.
  • The traveling salesman problem (TSP) is a classic combinatorial optimization challenge.

Purpose of the Study:

  • To simplify parameter setting in simulated annealing for the traveling salesman problem.
  • To introduce a novel list-based cooling schedule for enhanced SA performance.
  • To evaluate the effectiveness and parameter sensitivity of the proposed LBSA algorithm.

Main Methods:

  • Developed a list-based simulated annealing (LBSA) algorithm.
  • Implemented a novel list-based cooling schedule controlling temperature decrease.
  • Utilized a list of temperatures with Metropolis acceptance criterion.
  • Adapted the temperature list iteratively based on solution space topology.
  • Tested LBSA on benchmark TSP instances.

Main Results:

  • The LBSA algorithm simplifies parameter setting for TSP.
  • The list-based cooling schedule effectively controls temperature reduction.
  • LBSA demonstrates robust performance across various parameter values.
  • LBSA achieves competitive results compared to state-of-the-art algorithms.
  • Parameter sensitivity analysis confirms the schedule's effectiveness.

Conclusions:

  • The LBSA algorithm offers a simplified and effective approach to solving the TSP.
  • The novel list-based cooling schedule enhances SA performance and robustness.
  • LBSA provides a competitive alternative to existing TSP optimization methods.