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Journals / Computers and Informatics / 2025 / Volume: 5 Issue: 2

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

Gurcan Yavuz(DUMLUPINAR ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ, BİLGİSAYAR BİLİMLERİ ANABİLİM DALI)0000-0002-2540-1930
Halil İbrahim Çakir(KÜTAHYA DUMLUPINAR ÜNİVERSİTESİ)0000-0002-0564-6930
Pages
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Publication Date
2025-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

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Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:2015 Verileri
⚙️Variables:CEC 2015 · Crested porcupine optimization · Meta-Heuristics

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.

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Cite This Article

Yavuz, G., Çakir, H. İ. (2025). Performance analysis and comparison of crested porcupine optimization algorithm with state-of-the-art metaheuristics. Computers and Informatics, Volume: 5 Issue: 2. https://doi.org/10.62189/ci.1674492

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