Numerical assessment of the parallelization scalability on 200 MINLP benchmarks

This contribution addresses the question if and how the impact of parallelization can influence the performance of an evolutionary algorithm on constrained mixed-integer nonlinear optimization problems. On a set of 200 MINLP benchmarks the performance of the MIDACO solver is numerically assessed with gradually increasing parallelization factor from 1 to 100. The results demonstrate that the efficiency of the algorithm can be significantly improved by parallelized function evaluation. Furthermore, the results indicate that the scale-up behaviour on the efficiency resembles a linear nature, which implies that this approach will even be promising for very large parallelization factors. The presented research is especially relevant to cpu-time consuming real-world applications, where only a low number of serial processed function evaluation can be calculated in reasonable time.