<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This contribution presents a numerical evaluation of the impact of parallelization on the performance of an evolutionary algorithm for mixed-integer nonlinear programming (MINLP). On a set of 200 MINLP benchmarks the performance of the MIDACO solver is assessed with gradually increasing parallelization factor from one to three hundred. 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.</p></abstract>

This contribution presents a numerical evaluation of the impact of parallelization on the performance of an evolutionary algorithm for mixed-integer nonlinear programming (MINLP). On a set of 200 MINLP benchmarks the performance of the MIDACO solver is assessed with gradually increasing parallelization factor from one to three hundred. 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.

MIDACO Parallelization Scalability on 200 MINLP Benchmarks

Information Initiative Center, Hokkaido University Sapporo, Sapporo 060-0811,

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"MIDACO Parallelization Scalability on 200 MINLP Benchmarks"}

This contribution presents a numerical evaluation of the impact of parallelization on the performance of an evolutionary algorithm for mixed-integer nonlinear...

MIDACO Parallelization Scalability on 200 MINLP Benchmarks

Data publikacji: 20 mar 2017

Zakres stron: 171 - 181

Otrzymano: 16 wrz 2016

Przyjęty: 15 lis 2016

DOI: https://doi.org/10.1515/jaiscr-2017-0012

Słowa kluczowe
MINLP, optimization, MIDACO, parallelization

© 2017 Academy of Management (SWSPiZ), Lodz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

MIDACO Parallelization Scalability on 200 MINLP Benchmarks

Data publikacji: 20 mar 2017

Zakres stron: 171 - 181

Otrzymano: 16 wrz 2016

Przyjęty: 15 lis 2016

DOI: https://doi.org/10.1515/jaiscr-2017-0012

Słowa kluczoweMINLP, optimization, MIDACO, parallelization

© 2017 Academy of Management (SWSPiZ), Lodz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
MINLP, optimization, MIDACO, parallelization