Evaluation of random search optimization algorithms

It is difficult to evaluate a random search algorithms, because regardless of a chosen method of efficiency evaluation, in order to obtain reliable statistical estimates set of experiments and a detailed analysis of the obtained results is required. The work deals with an efficiency evaluation methods of random search algorithms for global optimization. A new algorithm assessment method based on the accuracy of the resulting solution, and the probability of it getting interdependence is proposed in this work. The proposed method has been validated by assessment of well-known random search optimization algorithms efficiency on a set of global optimization test problems. The experimental results showed that the proposed method to obtain estimates show the different algorithms behavioral characteristics such as the ability to find a global solution or attraction of local solutions. The method is also suitable for the efficient comparison of different algorithms efficiency. Thus, this paper presents a new algorithm assessment method, which uses the proposed hypervolume calculation principle and dominance relation. The proposed method provides additional information about the random search algorithms