Open AccessThe Improved Equilibrium Optimization Algorithm with Averaged Candidates
Author(s) -
Zheng-Ming Gao,
Juan Zhao,
Xuejun Tian
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 - 10.1088/1742-6596/1575/1/012105
Subject(s) - benchmark (surveying) , convergence (economics) , optimization algorithm , algorithm , mathematical optimization , computer science , mathematics , geodesy , economics , economic growth , geography
In this paper, we proposed the improvement of the newly raised equilibrium optimization (EO) algorithm by hybridizing the grey wolf optimization (GWO) algorithm. Simulation experiments were carried out and results showed that the hybrid EO algorithm with averaged candidates would perform better than the original one. Considering the better results and the smooth convergence curve on different types of benchmark functions, we recommend to withdraw the concept of equilibrium pool in the construction of the EO algorithm henceforth, and keep the guiding equation relevant to the averaged concentration of four best candidates instead.