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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Computer science

Background:

  • Dynamic combinatorial optimization problems (DCOPs) present significant computational challenges.
  • Metaheuristics are widely employed to find effective solutions for DCOPs within practical timeframes.
  • Self-adaptive strategies have emerged as a powerful mechanism for enhancing metaheuristic performance.

Purpose of the Study:

  • To improve the performance of a genetic algorithm (GA) for DCOPs.
  • To introduce and evaluate a novel self-adaptive mechanism within a GA framework.
  • To demonstrate the adaptability of the proposed mechanism in dynamic environments.

Main Methods:

  • A genetic algorithm (GA) was enhanced with a self-adaptive mechanism.
  • The genotype-phenotype mapping strategy was utilized.
  • Probabilistic distributions were employed for parameter definition.
  • The approach was tested on 3-SAT, One-Max, and Traveling Salesperson Problem (TSP) instances.

Main Results:

  • The self-adaptive mechanism significantly improved GA performance in dynamic settings.
  • The algorithm demonstrated robust adaptability across different DCOPs.
  • Effective parameter tuning was achieved through probabilistic distributions.

Conclusions:

  • The proposed self-adaptive mechanism is a viable strategy for enhancing GAs in dynamic environments.
  • The approach offers improved performance and adaptability for solving DCOPs.
  • This research contributes to the field of adaptive metaheuristics for optimization.