Performance analysis and comparison of crested porcupine optimization algorithm with state-of-the-art metaheuristics

Gurcan Yavuz; Halil İbrahim Çakir

doi:10.62189/ci.1674492

Performance analysis and comparison of crested porcupine optimization algorithm with state-of-the-art metaheuristics

Abstract

The Crested Porcupine Optimization (CrPO) algorithm is a recently developed nature-inspired metaheuristic based on the distinct multi-phase defense mechanisms observed in crested porcupines. This work evaluates the algorithm’s effectiveness using the CEC 2015 benchmark set, which comprises various test functions, including unimodal, multimodal, hybrid, and composition categories. CrPO introduces a population reduction strategy that operates cyclically, alongside four novel defense-modeled procedures, aiming to maintain a strong balance between exploration and exploitation phases. To assess its performance, the algorithm was benchmarked against four contemporary metaheuristics: Differential Evolution (DE), Love Evolution Algorithm (LEA), Hippopotamus Optimization (HipO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and the Aquila Optimizer (AO). Each test was conducted on both 10-dimensional and 30-dimensional instances, with 51 independent runs per problem to ensure statistical reliability. The empirical results indicate that CrPO outperforms its counterparts on the majority of test cases, securing the lowest (best) mean rank across both dimensional scales. Additionally, the superiority of CrPO was confirmed through statistical analysis using Friedman’s mean rank test, reinforcing its potential as a robust solution approach for tackling challenging numerical optimization tasks in scientific and engineering contexts.

Keywords

References

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Details

Primary Language

English

Subjects

Evolutionary Computation

Journal Section

Research Article

Authors

Gurcan Yavuz ^*
0000-0002-2540-1930
Türkiye

Halil İbrahim Çakir
0000-0002-0564-6930
Türkiye

Early Pub Date

December 15, 2025

Publication Date

December 31, 2025

Submission Date

April 11, 2025

Acceptance Date

November 17, 2025

Published in Issue

Year 2025 Volume: 5 Number: 2

DOI

https://doi.org/10.62189/ci.1674492

IZ

https://izlik.org/JA67FX83RM

Cite

RIS / Bibtex

Vancouver

1.Gurcan Yavuz, Halil İbrahim Çakir. Performance analysis and comparison of crested porcupine optimization algorithm with state-of-the-art metaheuristics. Computers and Informatics. 2025 Dec. 1;5(2):32-45. doi:10.62189/ci.1674492