Publication detail

Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization

KADLEC, P.

Original Title

Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization

Type

journal article in Web of Science

Language

English

Original Abstract

This paper aims to solve the space robot pathfinding problem, formulated as a multi-objective (MO) optimization problem with a variable number of dimensions (VND). This formulation enables the search and comparison of potential solutions with different model complexities within a single optimization run. A novel VND MO algorithm based on the well-known particle swarm optimization (PSO) algorithm is introduced and thoroughly described in this paper. The novel VNDMOPSO algorithm is validated on a set of 21 benchmark problems with different dimensionality settings and compared with two other state-of-the-art VND MO algorithms. Then, it is applied to solve five different instances of the space robot pathfinding problem formulated as a VND MO problem where two objectives are considered: (1) the minimal distance of the selected path, and (2) the minimal energy cost (expressed as the number of turning points). VNDMOPSO shows at least comparable or better convergence on the benchmark problems and significantly better convergence properties on the VND pathfinding problems compared with other VND MO algorithms.

Keywords

space robot; pathfinding; heuristic algorithm; particle swarm optimization; variable number of dimensions; metameric optimization

Authors

KADLEC, P.

Released

20. 6. 2023

Publisher

MDPI

Location

Basel, Switzerland

ISBN

1999-4893

Periodical

Algorithms

Year of study

16

Number

6

State

Swiss Confederation

Pages from

1

Pages to

20

Pages count

20

URL

https://www.mdpi.com/1999-4893/16/6/307/htm

Full text in the Digital Library

http://hdl.handle.net/11012/213599

BibTex

@article{BUT183839,
  author="Petr {Kadlec}",
  title="Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization",
  journal="Algorithms",
  year="2023",
  volume="16",
  number="6",
  pages="1--20",
  doi="10.3390/a16060307",
  issn="1999-4893",
  url="https://www.mdpi.com/1999-4893/16/6/307/htm"
}