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Engineering Optimization Using Evolutionary Algorithms: A Case Study on Hydro-thermal Power Scheduling

  • Kalvanmoy Deb
Part of the Natural Computing Series book series (NCS)

Abstract

Many engineering design and developmental activities finally resort to an optimization task which must be solved to get an efficient solution. These optimization problems involve a variety of complexities:
  • • Objectives and constraints can be non-linear, non-differentiable and discrete.

  • • Objectives and constraints can be non-stationary.

  • • Objectives and constraints can be sensitive to parameter uncertainties near the optimum.

  • • The number of objectives and constraints can be large.

  • • Objectives and constraints can be expensive to compute.

  • • Decision or design variables can be of mixed type involving continuous, discrete, Boolean, and permutations.

Keywords

Multiobjective Optimization Power Demand Power Generation Unit Engineer Optimization Thermal Unit 
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 2008

Authors and Affiliations

  • Kalvanmoy Deb
    • 1
  1. 1.Kanpur Genetic Algorithms Laboratory (Kan GAL)Indian Institute of TechnologyKanpurIndia

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