Research on path optimization of ant colony algorithm Improved Particle Swarm Optimization and Reverse Learning

DOI

10.2991/meees-18.2018.50How to use a DOI?

Keywords

Ant colony algorithm; Particle swarm optimization; reverse learning strategy; Pheromone update

Abstract

Aiming at the difficulty of determining the key parameters when applying ant colony optimization algorithm (ACO) to traveling salesman problem, we propose an improved particle swarm optimization (PSO) algorithm for adaptive parameter acquisition. Because repeated calls to ACO will increase the cost of computing and get the local optimal solution easily, the number of single ACO iterations is reduced, and the update of the pheromone is determined by the fitness function. After each call to ACO, the pheromone is not adjusted. In order to get better quality parameters of PSO, the reverse learning strategy is applied to PSO, and the speed of optimization is improved. The effectiveness of the algorithm is proved by the simulation experiment.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

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

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TY  - CONF
AU  - Shaobo Li
AU  - Kangqi Mu
AU  - Weimin Lin
AU  - Dong Sun
PY  - 2018/05
DA  - 2018/05
TI  - Research on path optimization of ant colony algorithm Improved Particle Swarm Optimization and Reverse Learning
BT  - Proceedings of the 2018 International Conference on Mechanical, Electrical, Electronic Engineering & Science (MEEES 2018)
PB  - Atlantis Press
SP  - 283
EP  - 289
SN  - 2352-5401
UR  - https://doi.org/10.2991/meees-18.2018.50
DO  - 10.2991/meees-18.2018.50
ID  - Li2018/05
ER  -