Grey Wolf Optimizer based on Nonlinear Adjustment Control Parameter

DOI

10.2991/icsma-16.2016.111How to use a DOI?

Keywords

Grey wolf optimizer, Control parameter, Nonlinear, Function optimization

Abstract

Grey wolf optimizer (GWO) is a relatively novel stochastic optimization technique which has bee shown to be competitive to other methods. However, the control parameterof GWO is decreased from 2 to 0 over the course of iterations. Inspired by particle swarm optimization (PSO), a novel nonlinear adjustment strategy of control parameter is designed to enhance the performance of GWO algorithm. In addition, to enhance the global convergence of GWO algorithm, when generating the initial population, opposition-based learning strategy is employed. Simulation results show that the proposed algorithm is able to provide very competitive results compared to other algorithms.

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  - Wen Long
PY  - 2016/12
DA  - 2016/12
TI  - Grey Wolf Optimizer based on Nonlinear Adjustment Control Parameter
BT  - Proceedings of the 2016 4th International Conference on Sensors, Mechatronics and Automation (ICSMA 2016)
PB  - Atlantis Press
SP  - 643
EP  - 648
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsma-16.2016.111
DO  - 10.2991/icsma-16.2016.111
ID  - Long2016/12
ER  -