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A Novel Cooperative Co-Evolutionary Algorithm for Large-Scale Constrained Optimization

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Fabiola Paz
Guillermo Leguizamón
Efrén Mezura-Montes
Published in
Computer Science – CACIC 2024
Publisher
Springer Nature Switzerland

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Abstract

This chapter introduces a novel cooperative co-evolutionary algorithm designed to tackle large-scale constrained optimization problems (LSCOPs). The algorithm, named CC-CEDG, combines the CEDG decomposition method with the CSaNSDE optimizer, addressing challenges such as high dimensionality and numerous constraints. The text delves into the background and related work, explaining the problem statement and various decomposition methods. It then describes the CC-CEDG algorithm in detail, including its decomposition, optimization, and cooperative combination stages. Experimental studies are presented, evaluating the algorithm's performance on benchmark functions with dimensions of 100, 500, and 1000. The results demonstrate that CC-CEDG outperforms other algorithms in terms of solution quality and success rate, particularly for highly separable or non-separable problems. The conclusion highlights the effectiveness of the CEDG decomposition method and the CSaNSDE optimizer in solving large-scale constrained optimization problems, suggesting future research directions for further improvement.
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previous chapter Analyzing Effectiveness and Interpretability of Machine Learning Models for Stress Detection
next chapter An Adaptative Agent-Based Model Metasystem for Emergency Departments
Title
A Novel Cooperative Co-Evolutionary Algorithm for Large-Scale Constrained Optimization
Authors
Fabiola Paz
Guillermo Leguizamón
Efrén Mezura-Montes
Copyright Year
2026
Book
Computer Science – CACIC 2024

Print ISBN: 978-3-032-00717-9

Electronic ISBN: 978-3-032-00718-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-00718-6_2
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