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Introduction

  • Ravipudi Venkata RaoEmail author
Chapter
  • 300 Downloads

Abstract

This chapter presents an introduction to the single objective and multi-objective optimization problems and the optimization techniques to solve the same. The a priori and a posteriori approaches of solving the multi-objective optimization problems are explained. The importance of algorithm-specific parameter-less concept is emphasized.

Keywords

Multi-objective optimizationMulti-objective Optimization Problem Jaya Algorithm TLBO Algorithm Gravitational Search Algorithm (GSA) Combined Objective Function 
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.

References

  1. Rao, R. V. (2007). Decision making in the manufacturing environment using graph theory and fuzzy multiple attribute decision making methods. London: Springer Verlag.Google Scholar
  2. Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43, 303–315.CrossRefGoogle Scholar
  3. Rao, R. V. (2016a). Teaching learning based optimization algorithm and its engineering applications. Switzerland: Springer International Publishing.CrossRefGoogle Scholar
  4. Rao, R. V. (2016b). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7, 19–34.Google Scholar
  5. Simon, D. (2013). Evolutionary optimization algorithms. New York: Wiley.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringS.V. National Institute of TechnologySuratIndia

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