Cohort Intelligence for Constrained Test Problems

  • Anand Jayant Kulkarni
  • Ganesh Krishnasamy
  • Ajith Abraham
Chapter
First Online:
Part of the Intelligent Systems Reference Library book series (ISRL, volume 114)

Abstract

Any optimization algorithm requires a technique/way to handle constraints. This is important because most of the real world problems are inherently constrained problems. There are a several traditional methods available such as feasibility-based methods, gradient projection method, reduced gradient method, Lagrange multiplier method, aggregate constraint method, feasible direction based method, penalty based method, etc. (Kulkarni and Tai in Int J Comput Intell Appl 10(4):445–470, 2011 [1]). According to Vanderplaat (Numerical optimization techniques for engineering design, 1984 [2]), the penalty based methods can be referred to as generalized constraint handling methods. They can be easily incorporated into most of the unconstrained optimization methods and can be used to handle nonlinear constraints.

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Anand Jayant Kulkarni
    • 1
    • 2
  • Ganesh Krishnasamy
    • 3
  • Ajith Abraham
    • 4
  1. 1.Odette School of BusinessUniversity of WindsorWindsorCanada
  2. 2.Department of Mechanical Engineering, Symbiosis Institute of TechnologySymbiosis International UniversityPuneIndia
  3. 3.Department of Electrical Engineering, Faculty of EngineeringUniversiti MalayaKuala LumpurMalaysia
  4. 4.Machine Intelligence Research Labs (MIR Labs)Scientific Network for Innovation and Research ExcellenceAuburnUSA

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