Abstract

For a high search accuracy and overcoming the problem of tangling the local optimum of grey wolf optimization (GWO) algorithm, a nonlinear convergence factor combining tangent and logarithmic functions is proposed to dynamically adjust the global search ability of the algorithm. An adaptive position updating strategy is also introduced to accelerate the convergence speed of the algorithm in the process of convergence. The experimental results on benchmark functions show that the improved algorithms outperform the standard grey wolf algorithm in convergence speed, stability and optimization accuracy.

Full Text

Published Version

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.