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

arXiv: https://arxiv.org/abs/1610.06009 · HTML: https://mlsanity.com/paper/1610.06009/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2016.

## Built on by (most cited first, 1 of 1)

- [Biblio-Analysis of Cohort Intelligence (CI) Algorithm and its allied applications from Scopus and Web of Science Perspective](https://mlsanity.com/paper/2209.03009/) (2022)

## Abstract (arXiv)

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.