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A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP.

Łukasz Strąk1, Rafał Skinderowicz1, Urszula Boryczka1

  • 1Institute of Computer Science, University of Silesia in Katowice, Będzińska 39, 41-205 Sosnowiec, Poland.

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Summary
This summary is machine-generated.

This study introduces a heterogeneous discrete particle swarm optimization (DPSO) algorithm for the dynamic traveling salesman problem (DTSP). The novel approach enhances solution quality by using diverse parameters and retaining information between problem instances.

Keywords:
discrete particle swarm optimizationdynamic traveling salesman problemheterogeneoushomogeneouspheromone

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Area of Science:

  • Optimization Algorithms
  • Computational Intelligence
  • Operations Research

Background:

  • The dynamic traveling salesman problem (DTSP) presents significant computational challenges due to its evolving nature.
  • Existing algorithms often struggle with adapting to changing problem instances efficiently.
  • Information retention between sequential problem instances is crucial for effective DTSP solutions.

Purpose of the Study:

  • To develop a novel discrete particle swarm optimization (DPSO) algorithm capable of handling the dynamic traveling salesman problem (DTSP).
  • To investigate the impact of heterogeneous (non-uniform) parameter values on DPSO performance for DTSP.
  • To enhance solution quality and population diversity in solving dynamic optimization problems.

Main Methods:

  • Modeling the DTSP as a sequence of static traveling salesman problem (TSP) sub-problems.
  • Implementing a discrete particle swarm optimization (DPSO) algorithm with heterogeneous parameter settings.
  • Utilizing a pheromone matrix to retain information across sequential sub-problems.
  • Developing an automated method for setting key DPSO parameters.

Main Results:

  • The heterogeneous DPSO algorithm demonstrated improved solution quality compared to a base DPSO.
  • The proposed algorithm achieved a higher population entropy, indicating increased diversity.
  • Performance was found to be competitive with established ant colony optimization (ACO) algorithms on DTSP instances.

Conclusions:

  • Heterogeneous parameter settings in DPSO positively impact solution quality for the DTSP.
  • The information retention mechanism enhances the algorithm's adaptability to dynamic environments.
  • The proposed heterogeneous DPSO offers a competitive alternative to existing ACO methods for DTSP.