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Most of the metaheuristics can efficiently solve unconstrained problems;however, their performance may degenerate if the constraints are involved. Thispaper proposes two constraint handling approaches for an emerging metaheuristicof Cohort Intelligence (CI). More specifically CI with static penalty functionapproach (SCI) and CI with dynamic penalty function approach (DCI) areproposed. The approaches have been tested by solving several constrained testproblems. The performance of the SCI and DCI have been compared with algorithmslike GA, PSO, ABC, d-Ds. In addition, as well as three real world problems frommechanical engineering domain with improved solutions. The results weresatisfactory and validated the applicability of CI methodology for solving realworld problems.

Constrained Cohort Intelligence using Static and Dynamic Penalty Function Approach for Mechanical Components Design