Comparison of Several Stochastic and Deterministic Derivative-Free Global Optimization Algorithms

  • Vladislav SovrasovEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11548)


In this paper popular open-source solvers are compared against Globalizer solver, which is developed at the Lobachevsky State University. The Globalizer is designed to solve problems with black-box objective function satisfying the Lipschitz condition and shows competitive performance with other similar solvers. The comparison is done on several sets of challenging multi-extremal benchmark functions. Also this work considers a method of heuristic hyperparameters control for the Globalizer allowing to reduce amount of initial tuning before optimization. The proposed scheme allows substantially increase convergence speed of the Globalizer by switching between “local” and “global” search phases in runtime.


Deterministic global optimization Stochastic global optimization Algorithms comparison Derivative-free algorithms Black-box optimization Multi-extremal problems 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Lobachevsky State University of Nizhni NovgorodNizhni NovgorodRussia

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