Open AccessThe improved mayfly optimization algorithm with opposition based learning rules
Author(s) -
Zheng-Ming Gao,
Juan Zhao,
Su-Ruo Li,
Yurong Hu
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 - 10.1088/1742-6596/1693/1/012117
Subject(s) - opposition (politics) , mayfly , algorithm , computer science , artificial intelligence , literal (mathematical logic) , machine learning , law , political science , nymph , politics , biology , botany
Literal researches proved that not only the best candidates or the best historical trajectories would perform well in guiding the individuals in swarms to find the best solution, the worst and the worst historical trajectories would also work well in doing so. Such situations could be directly treated as pairs of oppositions, and satisfied the ancient Chinese Yin-Yang philosophy, where the opposition based learning (OBL) rule was directly derived from. In this paper, the improved mayfly optimization (MO) algorithm with OBL rules were proposed, simulation experiments were carried out and results showed that the improved MO algorithm with OBL rules would perform better than usual.