JCLEC-MO: a Java suite for solving many-objective optimization engineering problems
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909123169222656 |
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| author | Ramírez, Aurora Romero, José Raúl García-Martínez, Carlos Ventura, Sebastián |
| author_facet | Ramírez, Aurora Romero, José Raúl García-Martínez, Carlos Ventura, Sebastián |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18616 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | JCLEC-MO: a Java suite for solving many-objective optimization engineering problems Ramírez, Aurora Romero, José Raúl García-Martínez, Carlos Ventura, Sebastián Neural and Evolutionary Computing Artificial Intelligence 68T20 D.0; I.2.8 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. |
| title | JCLEC-MO: a Java suite for solving many-objective optimization engineering problems |
| topic | Neural and Evolutionary Computing Artificial Intelligence 68T20 D.0; I.2.8 |
| url | https://arxiv.org/abs/2402.18616 |