  Most of the metaheuristics can efficiently solve unconstrained problems;
however, their performance may degenerate if the constraints are involved. This
paper proposes two constraint handling approaches for an emerging metaheuristic
of Cohort Intelligence (CI). More specifically CI with static penalty function
approach (SCI) and CI with dynamic penalty function approach (DCI) are
proposed. The approaches have been tested by solving several constrained test
problems. The performance of the SCI and DCI have been compared with algorithms
like GA, PSO, ABC, d-Ds. In addition, as well as three real world problems from
mechanical engineering domain with improved solutions. The results were
satisfactory and validated the applicability of CI methodology for solving real
world problems.
