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JCLEC-MO: a Java suite for solving many-objective optimization engineering problems

Aurora Ramírez, José Raúl Romero, Carlos García-Martínez, Sebastián Ventura

TL;DR

JCLEC-MO presents a Java-based, extensible framework for multi- and many-objective optimization that emphasizes modularity, problem independence, and industrial readiness. It combines MOEAs and MOPSO within a unified strategy-driven architecture, supports problem-specific elements, and integrates with external analytics tools such as R for post-processing. A detailed WRM case study demonstrates practical translation of real-world problems into modular components, configuration-driven experimentation, and comprehensive post-hoc analysis with statistical testing. The work advances practical tooling for engineers by offering a broad algorithm catalog, rich experimentation utilities, and seamless interoperability, paving the way for broader industrial adoption of MaOP optimization techniques.

Abstract

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.

JCLEC-MO: a Java suite for solving many-objective optimization engineering problems

TL;DR

JCLEC-MO presents a Java-based, extensible framework for multi- and many-objective optimization that emphasizes modularity, problem independence, and industrial readiness. It combines MOEAs and MOPSO within a unified strategy-driven architecture, supports problem-specific elements, and integrates with external analytics tools such as R for post-processing. A detailed WRM case study demonstrates practical translation of real-world problems into modular components, configuration-driven experimentation, and comprehensive post-hoc analysis with statistical testing. The work advances practical tooling for engineers by offering a broad algorithm catalog, rich experimentation utilities, and seamless interoperability, paving the way for broader industrial adoption of MaOP optimization techniques.

Abstract

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.
Paper Structure (19 sections, 2 equations, 5 figures, 6 tables, 1 algorithm)

This paper contains 19 sections, 2 equations, 5 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Related work
  4. General-purpose MOFs
  5. MOO-specific MOFs
  6. Architecture of JCLEC-MO
  7. Software modules
  8. Algorithms
  9. Problem-specific elements
  10. Experiments
  11. Quality indicators and reporters
  12. Utilities
  13. An illustrative running example
  14. Problem representation
  15. Translating the problem into code in JCLEC-MO
  16. ...and 4 more sections

Figures (5)

  • Figure 1: Overall module organization of JCLEC-MO
  • Figure 2: JCLEC-MO main classes and interfaces
  • Figure 3: Collaboration between algorithms and strategies
  • Figure 4: Pareto front found by each algorithm
  • Figure 5: Boxplots showing the distribution of unary indicators