In their original versions, nature-inspired algorithms for optimization such as evolutionary algorithms (EAs) and swarm intelligence algorithms (SIAs) are designed to sample unconstrained search spaces. Therefore, a considerable amount of research has been dedicated to adapt them to deal with constrained search spaces. The objective of the session is to present the most recent advances in constrained optimization using different nature-inspired algorithms.


The session seeks to promote the discussion and presentation of novel works related with (but not limited to) the following issues:

  • Novel constraint-handling techniques for EAs and SIAs
  • Constrained single- multi- and many-objective optimization
  • Constrained single- multi- and many-objective dynamic optimization
  • Constraint handling in multi-level equilibrium and optimization
  • Novel/adapted search algorithms for constrained optimization
  • Memetic algorithms in constrained search spaces
  • Parameter setting (tuning and control) in constrained optimization
  • Mixed (discrete-continuous) constrained optimization
  • Theoretical analysis and complexity of algorithms in constrained optimization
  • Convergence analysis in constrained optimization
  • Performance evaluation of algorithms in constrained optimization
  • Expensive constrained optimization
  • Design of scalable and challenging test functions for constrained optimization
  • Transfer learning in constrained optimization
  • Applications


Please follow the CEC 2021 instructions for authors.



Efrén Mezura-Montes

Artificial Intelligence Research Center, University of Veracruz, MEXICO.


Helio J.C. Barbosa

LNCC & Universidade Federal de Juiz de Fora, BRAZIL.


Rituparna Datta

Department of Computer Science, University of South Alabama, USA