Parallel and distributed architecture of genetic algorithm on Apache Hadoop and Spark - Lu, Hao-Chun; Hwang, F.J.; Huang, Yao-Huei (Applied soft computing, 2020)
The genetic algorithm (GA), one of the best-known metaheuristic algorithms, has been extensively utilized in various fields of management science, operational research, and industrial engineering. The efficiency of GAs in solving large-scale optimization problems would be enhanced if the iterative processes required by the genetic operators can be implemented in a parallel and distributed computing architecture. Apache Hadoop has recently been one of the most popular systems for distributed storage and parallel processing of big data. By integrating the GA highly into Apache Hadoop, this study proposes an advanced GA parallel and distributed computing architecture that achieves the effectiveness and efficiency of GA evolution. Characterized by the sophisticated mechanism of dispatching the GA core operators into Apache Hadoop, the developed computing framework fits well with the cloud computing model. The presented GA parallelization architecture outperforms the state-of-the-art reference architectures according to the computational experiments where the testing instances of traveling salesman problems are employed. Our numerical experiments also demonstrate that the proposed architecture can readily be extended to Apache Spark. •This study proposes an advanced GA parallel and distributed computing architecture.•The proposed framework dispatches GA core operators into the cloud computing model.•The proposed framework can be used in any parallel and distributed architecture.•The proposed architecture outperforms the reference architectures.