Numerical assessment of the parallelization scalability on 200 MINLP benchmarks
Martin Schlueter, Masaharu Munetomo · 2016
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.