Open AccessThe hybridized Harris hawk optimization and slime mould algorithm
Author(s) -
Juan Zhao,
Zheng-Ming Gao
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1682/1/012029
Subject(s) - benchmark (surveying) , convergence (economics) , algorithm , optimization algorithm , rate of convergence , computer science , mathematical optimization , mathematics , key (lock) , geography , cartography , computer security , economics , economic growth
Both the Harris hawk optimization (HHO) algorithm proposed in 2016 and the slime moud algorithm proposed recently had complicated disciplines for individuals to update their positions. And both of them were proved to be capable of finding the best solutions for either benchmark functions or real engineering problems. In this paper, we further hybridized the SM and HHO algorithms and allowed the individuals in swarms to take more ways to update their positions. Simulation experiments were carried out and the better performance in either accuracy or convergence rate verified the capability of the hybridization.