Population-based meta-heuristic algorithms are among the dominant algorithms used to solve challenging real world problems in diverse fields. Whale Optimization Algorithm (WOA) is a recent swarm intelligence meta-heuristic algorithm based on the bubble-net feeding behavior of humpback whales. Despite its capability to solve complex optimization problems, WOA requires enormous amount of computations when solving large size problems. This work proposes Spark-WOA, a distributed implementation of WOA on Apache Spark platform to enhance its performance and reduce computational complexity. The proposed algorithm exploits in-memory computations and broadcast features of Apache Spark to provide better performance and scalability. Details of the proposed algorithm are presented and its performance as compared to a recent Apache Hadoop implementation is discussed. Experimental results demonstrated the superiority of the proposed implementation in terms of both speed and scalability.

基于人口的元启发式算法是用于解决各领域挑战性现实问题的主要算法之一。鲸鱼优化算法（WOA）是一种最新的群体智能元启发式算法，基于座头鲸的气泡网进食行为。尽管具有解决复杂优化问题的能力，但是WOA在解决大型问题时仍需要进行大量计算。这项工作提出了Spark-WOA，它是Apache Spark平台上WOA的分布式实现，可以提高其性能并降低计算复杂性。所提出的算法利用Apache Spark的内存中计算和广播功能来提供更好的性能和可伸缩性。提出了该算法的细节，并讨论了与最近的Apache Hadoop实现相比的性能。实验结果证明了所提实施方案在速度和可伸缩性方面的优越性。