Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 89, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2019.103457
Keywords
Whale optimization algorithm; Refraction learning; High-dimensional optimization; Engineering design optimization; Photovoltaic model; Parameter estimation
Categories
Funding
- National Natural Science Foundation of China [61463009]
- Program for the Science and Technology Top Talents of Higher Learning Institutions of Guizhou, China [KY[2017]070]
- Scientific Research Foundation of Hunan Provincial Education Department [2019]
Ask authors/readers for more resources
Whale optimization algorithm (WOA) is a relatively new meta-heuristic optimization algorithm which mimics the hunting behavior of humpback whales. This paper presents a modified version of WOA, called RLWOA, for solving high-dimensional optimization problems. The proposed RLWOA adopts a modified conversion parameter update rule that relies on Logistic model to balance between diversity and convergence during the search process, and a new refraction-learning strategy based on the principle of refraction of light is proposed to help the population jump out of a local optimum. The experiments on a set of benchmark test functions with various features, i.e., 12 widely used benchmark functions with 100, 1000, and 10000 dimensions, two practical engineering design problems, and parameter estimation problem of photovoltaic model. The comparisons demonstrate that the proposed RLWOA shows better or at least competitive performance against the standard WOA, WOA variants and other state-of-the-art meta-heuristic algorithms for solving high-dimensional numerical optimization, practical engineering design optimization, and photovoltaic model parameter estimation problems.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available