Spatial information sampling: another feedback mechanism of realising adaptive parameter control in meta-heuristic algorithms Online publication date: Mon, 07-Feb-2022
by Haichuan Yang; Sichen Tao; Zhiming Zhang; Zonghui Cai; Shangce Gao
International Journal of Bio-Inspired Computation (IJBIC), Vol. 19, No. 1, 2022
Abstract: This paper innovatively proposes a spatial information sampling strategy to adaptively control the parameters of meta-heuristic algorithms (MHAs). The solutions' spatial distribution information in current iterations is used to control the parameters in the following iterations. An adaptive parameter control method requires obtaining information from the operation of MHAs and feeding it back to the adjustment of parameters. The mainstream information acquisition method is to record the changes to the solutions in the iterative process. In essence, the proposed feedback method, i.e., chaotic perceptron (CP), makes use of the temporal information arising from the change of solutions in MHAs. The wingsuit flying search algorithm and differential evolution are employed as case studies. Experimental results validate the effectiveness of the proposed strategy. The source code of CP can be found at https: //toyamaailab.github.io/.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Bio-Inspired Computation (IJBIC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com