Solution to constrained test problems using cohort intelligence algorithm

Abstract

Most of the real world problems are inherently constrained in nature. There are several nature inspired algorithms being developed; however their performance degenerate when applied solving constrained problems. This paper proposes Cohort Intelligence (CI) approach in which a probability based constrained handling approach is incorporated. This approach is tested by solving four well known test problems. The performance is compared and discussed with regard to the robustness, computational cost, standard deviation and rate of convergence etc. The constrained CI approach is used here to solve few inequality based constrained problems. The solution to these problems indicates that the CI approach can be further efficiently applied to solve a variety of practical/real world problems.