云环境下求解大规模优化问题的协同差分进化算法

差分进化是一种求解连续优化问题的高效算法。然而差分进化算法求解大规模优化问题时，随着问题维数的增加，算法的性能下降，且搜索时间呈指数上升。针对此问题，本文提出了一种新的基于Spark的合作协同差分进化算法（SparkDECC）。SparkDECC采用分治策略，首先通过随机分组方法将高维优化问题分解成多个低维子问题，然后利用Spark的弹性分布式数据模型，对每个子问题并行求解，最后利用协同机制得到高维问题的完整解。通过在13个高维测试函数上进行的对比实验和分析，实验结果表明算法加速明显且可扩展性好，验证了SparkDECC的有效性和适用性。

Cooperative differential evolution in cloud computing for solving large-scale optimization problems

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates quickly and the runtime grows exponentially when differential evolution is applied to solve large-scale optimization problems. To overcome this problem, a novel cooperative coevolution differential evolution based on Spark (called SparkDECC) was proposed. The strategy of separate processing is used in SparkDECC. Firstly, the large-scale problem is decomposed into several low-dimensional sub-problems by using the random grouping strategy; then each sub-problem can be tackled in a parallel way by taking advantage of the parallel computation capability of the resilient distributed datasets model in Spark; finally the optimal solution of the entire problem is obtained by using cooperation mechanism. The experimental results on 13 high-dimensional functions show that the new algorithm has good performances of speedup and scalability. The effectiveness and applicability of the proposed algorithm were verified.