Asynchronous surrogate-assisted optimization networks

This paper introduces a new, highly asynchronous method for surrogate-assisted optimization where it is possible to concurrently create surrogate models, evaluate fitness functions and do parameter optimization for the underlying problem, effectively eliminating sequential workflows of other surrogate-assisted algorithms. Using optimization networks, each part of the optimization process is exchangeable. First experiments are done for single objective benchmark functions, namely Ackley, Griewank, Schwefel and Rastrigin, using problem sizes starting from 2D up to 10D, and other EGO implementations are used as baseline for comparison. First results show that the implemented network approach is competitive to other EGO implementations in terms of achieved solution qualities and more efficient in terms of execution times.