## 3 Utilized Meta-Heuristic Algorithms
### Standard Genetic Algorithm
Although PSO and GWO provide decent performances, they are behind BIRA. ## 5 Conclusions

This study introduces novel swarm-based meta-heuristic algorithms, IRA, BRA, and BIRA, which are modeled on the mating behavior of chickens and roosters. First of all, these three new versions were compared among themselves in terms of performance and it was observed that BIRA offered more successful results. To evaluate the contribution of BIRA's performance to the literature, it is compared with well-known metaheuristic algorithms: SGA, DE, PSO, CS, and GWO. Additionally, RA was included in this assessment to highlight BIRA's superior performance compared to its predecessor (RA). This comparison involved the use of 20 widely recognized benchmark optimization functions, 11 CEC2014 test functions, and 17 CEC2018 test functions (which are also in the CE2020 test suite). Furthermore, [PERSON] and [PERSON] Signed Rank statistical \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Values** & **SGA** & **DE** & **PSO** & **CS** & **GWO** & **BIRA** \\ \hline \(x_{1}\) & 0.1262 & 0.1262 & 0.0793 & 0.1369 & 0.08622 & 0.08888 \\ \(x_{2}\) & 0.4283 & 0.4283 & 0.4350 & 0.3629 & 0.3235 & 0.2775 \\ \(x_{3}\) & 13.4159 & 13.4159 & 6.3168 & 4.9342 & 8.3225 & 2.3680 \\ Cost & 0.1050895 & 0.1050895 & 0.0227434 & 0.04714145 & 0.02482801 & **0.0095515** \\ \hline \hline \end{tabular}
\end{table} 
Table 14: The results for designing the tension/compression spring. Figure 7: Tension/compression spring design. tests were conducted to establish the significance of the results. Moreover, three real-world engineering problems, pressure vessel, gear train design, and tension/compression spring design, were also utilized to test the performance of the algorithms. When all the tests are examined, it is obvious that BIRA offers a much superior performance compared to RA. While the BIRA algorithm analyzes the search space better with the dance technique, it also tries to prevent it from getting stuck in local minima with its bipolar behavior. BIRA showed successful results not only compared to RA but also compared to all tested algorithms, albeit by a small margin compared to PSO. However, BIRA not only produced new values for studies in the field of real-world engineering problems, but also provided reasonable results. Thanks to the superior performance offered by BIRA, it has been proven that bipolar behavior has a good effect on meta-heuristic algorithms. Even though bipolar behavior is beneficial in reaching different points of the algorithm, it is obvious that the number of the worst individuals will decrease since the population will evolve throughout iterations. For this reason, one of the most important disadvantages of the algorithm is that the mutation phase, which allows the discovery of different points of an algorithm, is not included in this algorithm. Hence, a mutation phase will be added to the algorithm as a future work, and then, its performance will be analyzed in various optimization challenges. Moreover, based on the idea that bipolar behavior can improve the performance of meta-heuristic algorithms, bipolar behavior will be added to several meta-heuristics and the performance of these algorithms will be examined. This study contributes to the literature by providing a new perspective on algorithm design, demonstrating that bipolar behavior and dance-inspired movements can enhance optimization performance. These findings highlight the potential of biologically inspired techniques in advancing meta-heuristic algorithms and solving complex optimization problems more effectively. This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. **Conflicts of Interest:** The author declares no conflicts of interest.