Although metaheuristics have been widely recognized as efficient techniques to solve real-world optimization problems, implementing them from scratch remains difficult for domain-specific experts without programming skills. In this scenario, metaheuristic optimization frameworks are a practical alternative as they provide a variety of algorithms composed of customized elements, as well as experimental support. Recently, many engineering problems require to optimize multiple or even many objectives, increasing the interest in appropriate metaheuristic algorithms and frameworks that might integrate new specific requirements while maintaining the generality and reusability principles they were conceived for. Based on this idea, this paper introduces JCLEC-MO, a Java framework for both multi- and many-objective optimization that enables engineers to apply, or adapt, a great number of multi-objective algorithms with little coding effort. A case study is developed and explained to show how JCLEC-MO can be used to address many-objective engineering problems, often requiring the inclusion of domain-specific elements, and to analyze experimental outcomes by means of conveniently connected R utilities.

翻译：尽管元启发式算法已被广泛认可为解决实际优化问题的有效技术，但对于缺乏编程技能的领域专家而言，从零实现这些算法仍然存在困难。在此背景下，元启发式优化框架作为一种实用替代方案，提供了由定制化元素组成的多种算法及实验支持。近年来，许多工程问题需要优化多个甚至高维目标，这促使人们对能够整合新特定需求、同时保持通用性与可复用性设计原则的元启发式算法及框架产生兴趣。基于这一理念，本文介绍了JCLEC-MO——一个面向多目标与高维多目标优化的Java框架，使工程师能够以极少的编码工作量应用或适配大量多目标算法。通过开发并阐述案例研究，本文展示了如何利用JCLEC-MO解决常需融入领域特定元素的高维多目标工程问题，并通过便捷连接的R工具分析实验结果。