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)

Abstract

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.

Keywords

Particle Swarm Optimization Global Optimization Differential Evolution Constraint Violation Global Optimization Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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