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Hybridization of Evolutionary Operators with Elitist Iterated Racing for the Simulation Optimization of Traffic

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Hybrid algorithms combining IRACE and evolutionary operators optimize traffic light scheduling. Differential evolution hybrids excel with low simulation budgets, while IRACE alone is better for high budgets.

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Transportation Engineering

Background:

  • Traffic light scheduling requires robust solutions that perform well across diverse traffic scenarios.
  • Evaluating candidate solutions necessitates extensive simulation, making efficiency crucial.
  • Previous research highlights the effectiveness of combining Iterative Racing (IRACE) with evolutionary operators for numerical optimization in this domain.

Approach:

  • This study explores hybrid algorithms by combining evolutionary operators with elitist Iterative Racing (IRACE) for traffic light program simulation-optimization.
  • A literature review identified optimal evolutionary operators for this specific problem, leading to the proposal of novel hybrid algorithms.
  • The proposed methods were evaluated on a realistic case study involving the optimization of 275 traffic lights in Málaga, Spain.

Key Points:

  • Hybrid algorithms integrating IRACE with evolutionary operators were developed for traffic light scheduling.
  • The hybrid algorithm of IRACE plus differential evolution demonstrated superior performance with limited simulation budgets.
  • For extensive simulation budgets, standalone IRACE outperformed hybrid approaches, albeit with increased computation time.

Conclusions:

  • Hybrid algorithms offer a promising approach for robust traffic light scheduling, particularly when simulation resources are constrained.
  • The choice between hybrid methods and standalone IRACE depends on the available simulation budget and desired optimization time.
  • This research provides valuable insights into optimizing complex traffic control systems through advanced simulation-optimization techniques.