Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 13 May 2019 / Approved: 15 May 2019 / Online: 15 May 2019 (10:41:10 CEST)

A peer-reviewed article of this Preprint also exists.

Strąk, Ł.; Skinderowicz, R.; Boryczka, U.; Nowakowski, A. A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP. Entropy 2019, 21, 738. Strąk, Ł.; Skinderowicz, R.; Boryczka, U.; Nowakowski, A. A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP. Entropy 2019, 21, 738.

Journal reference: Entropy 2019, 21, 738
DOI: 10.3390/e21080738

Abstract

This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic travelling salesman problem (DTSP). The DTSP can be modelled as a sequence of static sub-problems, each of which is an instance of the TSP. We present a method for automatically setting the values of the DPSO parameters without three parameters, which can be defined based on the size of the problem, the size of the particle swarm, the number of iterations, and the particle neighbourhood size. We show that the diversity of parameter values has a positive effect on the quality of the generated results. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO.

Subject Areas

dynamic travelling salesman problem; pheromone; discrete particle swarm optimization; heterogeneous; homogeneous

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.