Verification of a hypothesis about unification and simplification for position updating formulas in particle swarm optimization

DOI

10.2991/wartia-16.2016.365How to use a DOI?

Keywords

particle swarm optimization (PSO), swarm intelligence, artificial intelligence, evolutionary computation, position updating, convergence

Abstract

Particle swarm optimization (PSO) has been a popular research area in artificial intelligence technology, where the two issues of theoretical analysis and premature convergence have been the focus of attention. However, due to complex dynamics in particle swarm, the former has been conducted only in simplified systems. And the latter has been dealt with only by introducing some additional operations, which inevitably increases the complexity of PSO and makes the theoretical analysis more difficult. To handle the above problems, we already proposed a unified and simplified formula for position updating in the existing algorithms, but that formula depends heavily on the hypothesis of that new positions of particles are centered on their weighted experience. In this paper, we selected ten algorithms that were widely used or more novel, and generated a large number of data samples to test their frequency histograms. The experiment results verified this hypothesis, and further proved the correctness of the unification and simplification for position updating formulas.

Copyright

© 2016, 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  - Jian Hu
PY  - 2016/05
DA  - 2016/05
TI  - Verification of a hypothesis about unification and simplification for position updating formulas in particle swarm optimization
BT  - Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications
PB  - Atlantis Press
SP  - 1839
EP  - 1844
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-16.2016.365
DO  - 10.2991/wartia-16.2016.365
ID  - Hu2016/05
ER  -