Splitting a population into multiple instances is a technique used extensively in recent years to help improve the performance of nature-inspired optimization algorithms. Work on those populations can be done in parallel, and they can interact asynchronously, a fact that can be leveraged to create scalable implementations based on, among other methods, distributed, multi-threaded, parallel, and cloud-native computing. However, the design of these cloud-native, distributed, multi-population algorithms is not a trivial task. Using as a foundation monolithic (single-instance) solutions, adaptations at several levels, from the algorithmic to the functional, must be made to leverage the scalability, elasticity, (limited) fault-tolerance, reproducibility, and cost-effectiveness of cloud systems while, at the same time, conserving the intended functionality. Instead of an evolutive approach, in this paper, we propose a cloud-native optimization framework created from scratch, that can include multiple (population-based) algorithms without increasing the number of parameters that need tuning. This solution goes beyond the current state of the art, since it can support different algorithms at the same time, work asynchronously, and also be readily deployable to any cloud platform. We evaluate this solution’s performance and scalability, together with the effect other design parameters had on it, particularly the number and the size of populations with respect to problem size. The implemented platform is an excellent alternative for running locally or in the cloud, thus proving that cloud-native bioinspired algorithms perform better in their “natural” environment than other algorithms, and set a new baseline for scaling and performance of this kind of algorithms in the cloud.

将种群分成多个实例是近年来广泛使用的一项技术，可帮助提高自然启发式优化算法的性能。这些种群上的工作可以并行完成，并且可以异步交互，这一事实可以利用分布式，多线程，并行和云原生计算等方法来创建可扩展的实现。但是，这些云原生的，分布式的，多人口算法的设计并不是一件容易的事。必须使用整体（单实例）解决方案作为基础，从算法到功能都需要在多个级别进行调整，以利用云系统的可伸缩性，弹性，（有限的）容错性，可再现性和成本效益。同时，保留预期的功能。在本文中，我们提出了一个从零开始创建的云原生优化框架，该框架可以包含多种（基于人口）算法，而无需增加需要调整的参数数量，而不是采用渐进方法。该解决方案超越了现有技术水平，因为它可以同时支持不同的算法，可以异步工作，还可以轻松部署到任何云平台。我们评估该解决方案的性能和可伸缩性，以及其他设计参数对其产生的影响，尤其是与问题大小相关的总体数量和规模。实施的平台是在本地或云中运行的绝佳选择，