We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms.

中文翻译：

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在异构分布式系统中使用遗传算法的多启发式动态任务分配。

我们提出了一种多启发式进化任务分配算法，以将任务动态映射到异构分布式系统中的处理器。它利用遗传算法，结合八种常见的启发式方法，以尽量减少总执行时间。它对批量未映射的任务进行操作，并可以抢先将任务重新映射到处理器。该算法已在 Java 分布式系统上实现，并通过一组来自生物信息学、生物医学工程、计算机科学和密码学领域的六个问题进行了评估。使用多达 150 个异构处理器的实验表明，该算法比其他最先进的启发式算法实现了更高的效率。