Cohort Intelligence for Constrained Test Problems

• Published in: Cohort Intelligence: A Socio-inspired Optimization Method Vol. 114; pp. 25 - 37
• Main Authors: Krishnasamy, Ganesh, Kulkarni, Anand Jayant, Abraham, Ajith
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2017; Springer International Publishing
• Series: Intelligent Systems Reference Library
• Subjects: Artificial intelligence
• ISBN: 3319442538; 9783319442532
• ISSN: 1868-4394; 1868-4408
• DOI: 10.1007/978-3-319-44254-9_3

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.