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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 14 Sayı: 2

Competitive hybrid Jaya and β-Hill Climbing algorithm for high-cost optimization problems

Yüksek maliyetli optimizasyon problemleri için rekabetçi hibrit Jaya ve β-Hill Climbing algoritması

Gurcan Yavuz(KÜTAHYA DUMLUPINAR ÜNİVERSİTESİ)0000-0002-2540-1930
Hatem Dumlu(KÜTAHYA DUMLUPINAR ÜNİVERSİTESİ)0000-0002-9056-4437
Yacoub Kadar Hassan(KÜTAHYA DUMLUPINAR ÜNİVERSİTESİ)0009-0005-2733-2114
Pages
597–606
Publication Date
2025-04-15
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AI Summary & Key Takeaways

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

According to the results, the Jaya-β-Hill Climbing variant (CT-JAYA-BH) obtained the best results.

📌 3 Key Takeaways

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Objective & Hypothesis:Jaya algorithm is a population-based parameter-less optimization algorithm.
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Key Findings & Data:These variants were tested on the CEC 2015 benchmark set provided by IEEE.
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Conclusion & Impact:According to the results, the Jaya-β-Hill Climbing variant (CT-JAYA-BH) obtained the best results.
🔬Design:Modeling & Simulation
📍Scope:2015 Verileri
⚙️Variables:Jaya · Yerel Arama · β-Hill Climbing

Abstract

Jaya algorithm is a population-based parameter-less optimization algorithm. It is frequently used in high-cost industrial and engineering problems. However, Jaya algorithm gets stuck in local optimum in some problems with constrained conditions. In this paper, we introduce a hybrid variant of Jaya to overcome the problem of getting stuck at the local optimum, the β-Hill Climbing local search algorithm is added to the Jaya algorithm and a hybrid Jaya algorithm (CT-JAYA-BH) is presented. Before deciding to use the β-Hill Climbing algorithm, Jaya was also combined with the Quasi Newton and Nelder-Mead local search algorithms. These variants were tested on the CEC 2015 benchmark set provided by IEEE. According to the results, the Jaya-β-Hill Climbing variant (CT-JAYA-BH) obtained the best results. A parameter analysis of CT-JAYA-BH was also performed to determine at which parameter values this variant achieved the best results. Moreover CT-JAYA-BH was compared with 14 different optimization algorithms using the CEC 2015 benchmark set. According to the results, the proposed CT-JAYA-BH algorithm outperforms the other algorithms with an average rank value of 1.87 in both 10 and 30 dimensions. The results show that CT-JAYA-BH is a highly competitive.

Özet

Jaya algoritması, popülasyon tabanlı parametresiz bir optimizasyon algoritmasıdır. Yüksek maliyetli endüstriyel ve mühendislik problemlerinde sıklıkla kullanılmaktadır. Ancak, Jaya algoritması kısıtlı koşullara sahip bazı problemlerde yerel optimumda takılıp kalmaktadır. Bu makalede, yerel optimumda takılma probleminin üstesinden gelmek için Jaya'nın hibrit bir varyantı tanıtılmakta, β -Hill Climbing yerel arama algoritması Jaya algoritmasına eklenmekte ve hibrit bir Jaya algoritması (CT-JAYA-BH) sunulmaktadır. β-Hill Climbing algoritmasını kullanmaya karar vermeden önce Jaya, Quasi Newton ve Nelder-Mead yerel arama algoritmaları ile de birleştirilmiştir. Bu varyantlar IEEE tarafından sağlanan CEC 2015 benchmark seti üzerinde test edilmiştir. Sonuçlara göre, Jaya-β-Hill Climbing varyantı (CT-JAYA-BH) en iyi sonuçları elde etmiştir. Bu varyantın hangi parametre değerlerinde en iyi sonuçları elde ettiğini belirlemek için CT-JAYA-BH'nin bir parametre analizi de yapılmıştır. Ayrıca CT-JAYA-BH, CEC 2015 kıyaslama seti kullanılarak 14 farklı optimizasyon algoritması ile karşılaştırılmıştır. Sonuçlara göre, önerilen CT-JAYA-BH algoritması hem 10 hem de 30 boyutta ortalama 1.87 rank değeri ile diğer algoritmalardan daha iyi performans göstermiştir. CT-JAYA-BH oldukça rekabetçidir.

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Cite This Article

Yavuz, G., Dumlu, H., Hassan, Y. K. (2025). Competitive hybrid Jaya and β-Hill Climbing algorithm for high-cost optimization problems. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 14 Sayı: 2, 597–606. https://doi.org/10.28948/ngumuh.1570577

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