Optimization can be defined as the operation of finding the best solution for a problem. This operation is performed by changing the initial parameters using existing data. There are various optimization algorithms to solve these kinds of problems; however, it cannot be expected that all optimization algorithms offer a proper and effective solution to all optimization problems. Therefore, it is necessary to select the proper algorithm by using similar benchmark functions to the problem, and to determine the best parameter values for the selected algorithm. In this study, a test tool that can run on the devices using the Windows, OS X, Android, and IOS operating systems was developed for eight different optimization algorithms: genetic, artificial immune, differential evolution, particle swarm optimization, simulated annealing, tabu search, artificial bee colony, and ant colony optimization algorithms. Six Hump Camel Back, Rastrigin, Shubert, Schwefel, and Drop Wave were the preferred benchmark functions. Special user defined special functions were also permitted. The developed test tool was tested for all the optimization algorithms in different platforms. In spite of the small differences in the running times, the results show that the tool can easily be used in Windows, OS X, Android, and IOS devices. (C) 2016 Wiley Periodicals, Inc.