Searching for common patterns on protein sequences by means of a parallel hybrid honey-bee mating optimization algorithm

David L. González-Álvarez, Miguel A. Vega-Rodríguez, Álvaro Rubio-Largo

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


At present, most computing systems have multiple cores accessing to a shared memory space. In the case of computing systems devoted to research, it is common to cluster several shared memory architectures together in order to go beyond the limits of a single shared memory architecture. These clusters can represent a significant resource for those researchers who accomplish to exploit them to the fullest. One of the most applied techniques to achieve this purpose is the mixed mode parallel programming which combines both OpenMP and MPI paradigms. In this paper, we present a parallel implementation of a swarm-based evolutionary algorithm designed for solving a complex biological optimization problem. In order to obtain the maximum possible performance, we have combined MPI and OpenMP. Furthermore, in order to solve the addressed problem in a realistic way, we have applied multiobjective optimization. For assessing the performance achieved by our proposal we have conducted experiments under different systems on six biological instances with different sizes. Results point out the relevance of combining mixed mode parallel programming, a swarm-based evolutionary algorithm, and multiobjective optimization from both parallelism and biology points of view.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalParallel Computing
Issue numberAugust
Publication statusPublished - 1 Aug 2018


  • Bioinformatics
  • Evolutionary computation
  • Multiobjective optimization
  • Parallel computing
  • Proteins


Dive into the research topics of 'Searching for common patterns on protein sequences by means of a parallel hybrid honey-bee mating optimization algorithm'. Together they form a unique fingerprint.

Cite this