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.

遗传算法（GA）是最著名的元启发式算法之一，已广泛用于管理科学，运筹学和工业工程的各个领域。如果遗传算子所需的迭代过程可以在并行和分布式计算体系结构中实现，则遗传算法解决大规模优化问题的效率将得到提高。Apache Hadoop最近成为用于大数据的分布式存储和并行处理的最受欢迎的系统之一。通过将GA高度集成到Apache Hadoop中，本研究提出了一种先进的GA并行和分布式计算架构，该架构可实现GA演化的有效性和效率。以将GA核心运算符分配到Apache Hadoop中的复杂机制为特征，开发的计算框架非常适合云计算模型。根据采用旅行商问题的测试实例的计算实验，提出的GA并行化体系结构优于最新的参考体系结构。我们的数值实验还表明，提出的体系结构可以轻松扩展到Apache Spark。