On Challenging Techniques for Constrained Global Optimization

  • Isabel A. C. P. Espírito Santo
  • Lino Costa
  • Ana Maria A. C. Rocha
  • M. A. K. Azad
  • Edite M. G. P. Fernandes
Part of the Intelligent Systems Reference Library book series (ISRL, volume 38)


This chapter aims to address the challenging and demanding issue of solving a continuous nonlinear constrained global optimization problem. We propose four stochastic methods that rely on a population of points to diversify the search for a global solution: genetic algorithm, differential evolution, artificial fish swarm algorithm and electromagnetism-like mechanism. The performance of different variants of these algorithms is analyzed using a benchmark set of problems. Three different strategies to handle the equality and inequality constraints of the problem are addressed. An augmented Lagrangian-based technique, the tournament selection based on feasibility and dominance rules, and a strategy based on ranking objective and constraint violation are presented and tested. Numerical experiments are reported showing the effectiveness of our suggestions. Two well-known engineering design problems are successfully solved by the proposed methods.


Particle Swarm Optimization Global Optimization Differential Evolution Constraint Violation Global Optimization Problem 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Isabel A. C. P. Espírito Santo
    • 1
  • Lino Costa
    • 1
  • Ana Maria A. C. Rocha
    • 1
  • M. A. K. Azad
    • 2
  • Edite M. G. P. Fernandes
    • 2
  1. 1.Department of Production and SystemsUniversity of MinhoBragaPortugal
  2. 2.Algoritmi R&D CentreUniversity of MinhoBragaPortugal

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