  Stochastic optimization algorithms are often used to solve complex
large-scale optimization problems in various fields. To date, there have been a
number of stochastic optimization algorithms such as Genetic Algorithm, Cuckoo
Search, Tabu Search, Simulated Annealing, Particle Swarm Optimization, Ant
Colony Optimization, etc. Each algorithm has some advantages and disadvantages.
Currently, there is no study that can help researchers to choose the most
popular optimization algorithm to deal with the problems in different research
fields. In this note, a quantitative analysis of the popularity of 14
stochastic optimization algorithms in 18 different research fields in the last
ten years from 2007 to 2017 is provided. This quantitative analysis can help
researchers/practitioners select the best optimization algorithm to solve
complex large-scale optimization problems in the fields of Engineering,
Computer science, Operations research, Mathematics, Physics, Chemistry,
Automation control systems, Materials science, Energy fuels, Mechanics,
Telecommunications, Thermodynamics, Optics, Environmental sciences ecology,
Water resources, Transportation, Construction building technology, and
Robotics.
