A new Particle Swarm Optimization Algorithm based on Periodic Changing Inertia Weight

DOI

10.2991/jimec-16.2016.39How to use a DOI?

Keywords

inertia weight; continuous decline; period; large range search; small range search

Abstract

The new inertia weight based on periodic linear change, repeat the change process from 0.9226 to 0.6226 until it reaches the maximum generation. At the beginning, the position of the particle change fast, when the given value is reached, the particle's position change with a fuzzy formula. Using the new algorithm, the global and local searching ability of the particle swarm optimization algorithm are improved obviously, the searching accuracy is also improved. To a certain extent, the premature convergence problem of particle swarm algorithm is solved. The algorithm also could be used to solve some complex computational problems in many fields.

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  - Mingyan Zheng
AU  - Siyan Wang
AU  - Yan Li
PY  - 2016/10
DA  - 2016/10
TI  - A new Particle Swarm Optimization Algorithm based on Periodic Changing Inertia Weight
BT  - Proceedings of the 2016 Joint International Information Technology, Mechanical and Electronic Engineering
PB  - Atlantis Press
SP  - 234
EP  - 238
SN  - 2352-5401
UR  - https://doi.org/10.2991/jimec-16.2016.39
DO  - 10.2991/jimec-16.2016.39
ID  - Zheng2016/10
ER  -