Open AccessThe negative mayfly optimization 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 - 10.1088/1742-6596/1693/1/012098
Subject(s) - mayfly , benchmark (surveying) , particle swarm optimization , algorithm , swarm intelligence , literal (mathematical logic) , global optimization , computer science , swarm behaviour , mathematical optimization , mathematics , artificial intelligence , geography , ecology , nymph , biology , geodesy
The global best or historical best positions were involved in updating positions of individuals in swarms for almost all of the swarm-based nature-inspired algorithms with exceptions for the averages. However, literal research to the particle swarm optimization (PSO) algorithm had proved that the all of the candidates would also leave the global best and the historical best candidates and such improvement would result in a better performance. Similarly, the negative mayfly optimization (MO) algorithm was proposed based on such conditions. Simulation experiments were carried out and verified that the negative MO algorithm could perform better than the original MO algorithm, especially in optimizing the multimodal benchmark functions.