Next Article in Journal
Fluid Flow and Entropy Generation Analysis of Al2O3–Water Nanofluid in Microchannel Plate Fin Heat Sinks
Previous Article in Journal
Electron Traversal Times in Disordered Graphene Nanoribbons
Previous Article in Special Issue
User-Oriented Summaries Using a PSO Based Scoring Optimization Method
Open AccessArticle

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

Institute of Computer Science, University of Silesia in Katowice, Będzińska 39, 41-205 Sosnowiec, Poland
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(8), 738; https://doi.org/10.3390/e21080738
Received: 12 June 2019 / Revised: 23 July 2019 / Accepted: 24 July 2019 / Published: 27 July 2019
(This article belongs to the Special Issue Unconventional Methods for Particle Swarm Optimization)
This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic traveling salesman problem (DTSP). The DTSP can be modeled as a sequence of static sub-problems, each of which is an instance of the TSP. In the proposed DPSO algorithm, the information gathered while solving a sub-problem is retained in the form of a pheromone matrix and used by the algorithm while solving the next sub-problem. We present a method for automatically setting the values of the key DPSO parameters (except for the parameters directly related to the computation time and size of a problem).We show that the diversity of parameters values has a positive effect on the quality of the generated results. Furthermore, the population in the proposed algorithm has a higher level of entropy. 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. View Full-Text
Keywords: dynamic traveling salesman problem; pheromone; discrete particle swarm optimization; heterogeneous; homogeneous dynamic traveling salesman problem; pheromone; discrete particle swarm optimization; heterogeneous; homogeneous
Show Figures

Graphical abstract

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop