The range of applications of artificial intelligence (AI) that is based on nature-inspired metaheuristics is rapidly increasing across various scientific fields as it is used to solve complex engineering problems. This work develops a cloud evolutionary machine learning system, called the nature-inspired metaheuristic optimization and prediction system (NiMOPS) that is composed of metaheuristic AI and web modules. The objective of the proposed system is to provide a user-friendly web analytics for making efficient, effective, and accurate predictions as solutions to engineering problems. For the purposes of web development, this work connects two programming languages, which are MATLAB and Java. A MATLAB Compiler is used to package the system into Java Archive (JAR) files, which provide the core modules for the development of the NiMOPS website using an integrated development environment (IDE). IDE compiles the JAR files, and web utilities (JavaScript, CSS, Servlet, and other utility files) to form the response-request connection between the user and the server. Therefore, the web-based system does not require the installation of an application by the users because they can access the cloud computing system ubiquitously with a browser or mobile device. Furthermore, it has many functions, including export—import file, train model, optimize prediction, save model and visualize results. Several case studies of this system, involving classification and regression problems, were examined. The analytic results of using the system to solve classification problems revealed that the system had a fault diagnosis accuracy of 96.5% and an accidental small fire accuracy of 52.4%. In solving regression problems, the root mean square errors were 28.58–68.82% better than those of previous methods. In particular, the proposed system performed multiple performance measures that were utilized in a regression analysis and were found to be more reliable evaluation metrics than used in elsewhere. The numerical experiments verified that cloud computing provides an innovative way to enable decision-makers to solve engineering problems.

基于自然启发式元启发式技术的人工智能（AI）的应用范围在各个科学领域都在迅速增长，因为它被用于解决复杂的工程问题。这项工作开发了一种云进化机器学习系统，称为自然启发式元启发式优化和预测系统（NiMOPS），该系统由元启发式AI和Web模块组成。提出的系统的目标是提供一种用户友好的Web分析，以进行有效，有效和准确的预测，作为对工程问题的解决方案。出于Web开发的目的，本文将MATLAB和Java这两种编程语言联系在一起。使用MATLAB编译器将系统打包为Java归档（JAR）文件，其中提供了使用集成开发环境（IDE）开发NiMOPS网站的核心模块。IDE编译JAR文件和Web实用程序（JavaScript，CSS，Servlet和其他实用程序文件）以形成用户和服务器之间的响应请求连接。因此，基于Web的系统不需要用户安装应用程序，因为他们可以使用浏览器或移动设备无处不在地访问云计算系统。此外，它具有许多功能，包括导出-导入文件，训练模型，优化预测，保存模型和可视化结果。研究了该系统的一些案例研究，涉及分类和回归问题。使用该系统解决分类问题的分析结果表明，该系统的故障诊断准确度为96.5％，偶然小火灾准确度为52.4％。在解决回归问题时，均方根误差比以前的方法好28.58–68.82％。特别是，所提出的系统执行了多项性能指标，这些指标在回归分析中得到了利用，并且被认为是比其他地方更可靠的评估指标。数值实验证明，云计算提供了一种创新的方法，使决策者能够解决工程问题。该提议的系统执行了多项性能指标，这些指标被用于回归分析中，并且被发现比其他地方的指标更可靠。数值实验证明，云计算提供了一种创新的方法，使决策者能够解决工程问题。该提议的系统执行了多项性能指标，这些指标被用于回归分析中，并且被发现比其他地方的指标更可靠。数值实验证明，云计算提供了一种创新的方法，使决策者能够解决工程问题。