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Stochastic optimization algorithms are often used to solve complexlarge-scale optimization problems in various fields. To date, there have been anumber of stochastic optimization algorithms such as Genetic Algorithm, CuckooSearch, Tabu Search, Simulated Annealing, Particle Swarm Optimization, AntColony Optimization, etc. Each algorithm has some advantages and disadvantages.Currently, there is no study that can help researchers to choose the mostpopular optimization algorithm to deal with the problems in different researchfields. In this note, a quantitative analysis of the popularity of 14stochastic optimization algorithms in 18 different research fields in the lastten years from 2007 to 2017 is provided. This quantitative analysis can helpresearchers/practitioners select the best optimization algorithm to solvecomplex 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, andRobotics.

A Note On The Popularity of Stochastic Optimization Algorithms in Different Fields: A Quantitative Analysis from 2007 to 2017