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

TL;DR

This paper introduces static and dynamic penalty function methods to enhance Cohort Intelligence metaheuristic for solving constrained mechanical design problems, demonstrating improved performance over other algorithms.

Contribution

It proposes novel constraint handling approaches for Cohort Intelligence, specifically static and dynamic penalty functions, and validates their effectiveness on real-world mechanical engineering problems.

Findings

SCI and DCI outperform traditional algorithms on test problems

Both approaches effectively solve real-world mechanical design problems

Dynamic penalty approach shows superior adaptability in constrained scenarios

Abstract

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.