Multi-objective optimization problems frequently appear in many diverse research areas and application domains. Metaheuristics, as efficient techniques to solve them, need to be easily accessible to users with different expertise and programming skills. In this context, metaheuristic optimization frameworks are helpful, as they provide popular algorithms, customizable components and additional facilities to conduct experiments. Due to the broad range of available tools, this paper presents a systematic evaluation and experimental comparison of 10 frameworks, covering from multi-purpose, consolidated tools to recent libraries specifically designed for multi-objective optimization. The evaluation is organized around seven characteristics: search components and techniques, configuration, execution, utilities, external support and community, software implementation and performance. An analysis of code metrics and a series of experiments serves to assess the last two features. Lesson learned and open issues are also discussed as part of the comparative study. The outcomes of the evaluation process reveal a contrasted support to recent advances in multi-objective optimization, with a lack of novel algorithms and variety of metaheuristics other than evolutionary algorithms. The experimental comparison also reports significant differences in terms of both execution time and memory usage under demanding configurations.

中文翻译：

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用于多目标优化的元启发式框架的实验比较

多目标优化问题经常出现在许多不同的研究领域和应用领域。元启发式作为解决这些问题的有效技术，需要具有不同专业知识和编程技能的用户可以轻松访问。在这种情况下，元启发式优化框架很有用，因为它们提供了流行的算法、可定制的组件和额外的设施来进行实验。由于可用工具范围广泛，本文对 10 个框架进行了系统评估和实验比较，涵盖从多用途、综合工具到专门为多目标优化设计的最新库。评估围绕七个特征进行组织：搜索组件和技术、配置、执行、实用程序、外部支持和社区，软件实现和性能。代码指标分析和一系列实验用于评估最后两个特性。经验教训和未解决的问题也作为比较研究的一部分进行了讨论。评估过程的结果揭示了对多目标优化最新进展的对比支持，除了进化算法之外，缺乏新颖的算法和各种元启发式算法。实验比较还报告了在要求苛刻的配置下执行时间和内存使用方面的显着差异。评估过程的结果揭示了对多目标优化最新进展的对比支持，除了进化算法之外，缺乏新颖的算法和各种元启发式算法。实验比较还报告了在要求苛刻的配置下执行时间和内存使用方面的显着差异。评估过程的结果揭示了对多目标优化最新进展的对比支持，除了进化算法之外，缺乏新颖的算法和各种元启发式算法。实验比较还报告了在要求苛刻的配置下执行时间和内存使用方面的显着差异。