Optimization problems arise nowadays in all disciplines, not only in the scientific area but also in the field of engineering or economics, and in many others. Currently, challenging optimization problems require solution methods that consume a significant amount of computational resources. The application of High-Performance Computing techniques is a common approach to obtain efficient implementations in traditional parallel computing systems. However, more recent approaches are exploring distributed programming frameworks developed in recent years to achieve efficient computations on clusters and cloud systems. In this paper we present a parallel implementation of the enhanced Scatter Search metaheuristic using Spark. The parallel program was obtained as a particularization of a general software framework we developed to support different realisations of the Scatter Search metaheuristic. The aim of this paper is to provide helpful guidance to readers interested in applying, or developing their own, parallel metaheuristics to solve challenging problems in the Cloud. With the twofold objective of demonstrating the potential of the parallelization with Spark and also of studying the factors that influence the performance of the solution, the proposal has been thoroughly evaluated on two different platforms, a cluster and a cloud platform, using a representative set of parameter estimation problems in the field of Computational Systems Biology.

如今，不仅在科学领域，而且在工程或经济学领域以及许多其他领域，优化问题都出现在所有学科中。当前，具有挑战性的优化问题需要消耗大量计算资源的解决方法。高性能计算技术的应用是在传统并行计算系统中获得有效实现的一种常用方法。但是，越来越多的方法正在探索近年来开发的分布式编程框架，以在集群和云系统上实现高效的计算。在本文中，我们介绍了使用Spark并行执行增强型分散搜索元启发式方法。并行程序是作为通用软件框架的特殊化而获得的，我们开发了通用软件框架来支持散点搜索元启发式算法的不同实现。本文的目的是为有兴趣应用或开发自己的并行元启发式方法以解决云中难题的读者提供有用的指导。为了展示与Spark并行化的潜力，并研究影响解决方案性能的因素，该提案已在两个不同的平台（群集和云平台）上进行了全面评估，使用了一套代表性的解决方案。计算生物学领域的参数估计问题。本文的目的是为有兴趣应用或开发自己的并行元启发式方法以解决云中难题的读者提供有用的指导。为了展示与Spark并行化的潜力，并研究影响解决方案性能的因素，该提案已在两个不同的平台（群集和云平台）上进行了全面评估，使用了一组代表性的解决方案。计算生物学领域的参数估计问题。本文的目的是为有兴趣应用或开发自己的并行元启发式方法以解决云中难题的读者提供有用的指导。为了展示与Spark并行化的潜力，并研究影响解决方案性能的因素，该提案已在两个不同的平台（群集和云平台）上进行了全面评估，使用了一套代表性的解决方案。计算生物学领域的参数估计问题。