Multi-Objective Particle Swarm Optimization Algorithm for the Minimum Constraint Removal Problem

DOI

10.2991/ijcis.d.200310.005How to use a DOI?

Keywords

Minimum constraint removal; Minimum constraint set; Path planning; Multi-objective optimization; Multi-objective particle swarm optimization algorithm

Abstract

This paper proposes a multi-objective approach for the minimum constraint removal (MCR) A problem. First, a multi-objective model for MCR path planning is constructed. This model takes into account factors such as the minimum constraint set, the route length, and the cost. A multi-objective particle swarm optimization (MOPSO) algorithm is then designed based on the fitness function of the multi-objective MCR problem, and an iteration formula based on the personal best (pbest) and global best (gbest) of the algorithm is constructed to update the particle velocity and position. Finally, compared with ant colony optimization (ACO) A and the crow search algorithm (CSA) A, the experimental results show that the MOPSO-based path planning algorithm can find a shorter path that traverses fewer obstacle areas and can thus perform MCR path planning more effectively.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

TY  - JOUR
AU  - Bo Xu
AU  - Feng Zhou
AU  - Antonio Marcel Gates
PY  - 2020
DA  - 2020/03/16
TI  - Multi-Objective Particle Swarm Optimization Algorithm for the Minimum Constraint Removal Problem
JO  - International Journal of Computational Intelligence Systems
SP  - 291
EP  - 299
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200310.005
DO  - 10.2991/ijcis.d.200310.005
ID  - Xu2020
ER  -