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Dynamic optimization problems are a class of optimization problems where the objective function, constraints, or both can change over time. In this work, we address the dynamic travelling salesman problem (TSP) by using the age-layered population structure (ALPS). To enhance different layer’s behaviour, we introduce the self-adaptation strategies to adjust the mutation and crossover rate in each layers, which are stationary in the conventional ALPS. The proposed strategies are compared with the stationary strategy over 7 different dynamic TSPs. The experimental results shows that the proposed strategy, convex strategy, yields much better results than the stationary strategy on complex problems.

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Age-Layer-Population-Structure with Self-adaptation in Optimization

  • 会议论文

  • 发表日期：

    2025年

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  • Kaifeng Yang,
  • Bernhard Werth,
  • Michael Affenzeller

摘要

Dynamic optimization problems are a class of optimization problems where the objective function, constraints, or both can change over time. In this work, we address the dynamic travelling salesman problem (TSP) by using the age-layered population structure (ALPS). To enhance different layer’s behaviour, we introduce the self-adaptation strategies to adjust the mutation and crossover rate in each layers, which are stationary in the conventional ALPS. The proposed strategies are compared with the stationary strategy over 7 different dynamic TSPs. The experimental results shows that the proposed strategy, convex strategy, yields much better results than the stationary strategy on complex problems.