Parallel CUDA implementation of the desirability-based scalarization approach for multi-objective optimization problems

Akca E., ALTINÖZ Ö. T., Emel S. U., YILMAZ A. E., EFE M., Yaylagul T.

Student Workshop on Bioinspired Optimization Methods and their Applications, BIOMA 2014, Ljubljana, Slovenia, 13 September 2014, pp.93-104 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Ljubljana
  • Country: Slovenia
  • Page Numbers: pp.93-104
  • Keywords: Aggre gation, CUDA, Desirability functions, Genetic algorithm, GPGPU programming, Multi-objective optimization, Parallelization, Scalarization
  • TED University Affiliated: No

Abstract

In this study, we present the results obtained for the parallel CUDA implementation of the previously proposed desirability-based scalarization approach for the solution of the multi-objective optimization problems. Our simulations show that compared to the sequential Java implementation, it is possible to find the same solutions (up to 16-time faster manner) by parallel CUDA implementation. We also try to outline our experiences of troubleshooting throughout the implementation as guidelines for upcoming researchers working in the same field.