TY  - JOUR
T1  - Competitive hybrid Jaya and β-Hill Climbing algorithm for high-cost optimization problems
TT  - Yüksek maliyetli optimizasyon problemleri için rekabetçi hibrit Jaya ve β-Hill Climbing algoritması
AU  - Dumlu, Hatem
AU  - Yavuz, Gurcan
AU  - Hassan, Yacoub Kadar
PY  - 2025
DA  - April
Y2  - 2025
DO  - 10.28948/ngumuh.1570577
JF  - Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
JO  - NÖHÜ Müh. Bilim. Derg.
PB  - Niğde Ömer Halisdemir Üniversitesi
WT  - DergiPark
SN  - 2564-6605
SP  - 597
EP  - 606
VL  - 14
IS  - 2
LA  - en
AB  - 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.
KW  - Jaya
KW  - Local Search
KW  - β -Hill Climbing
KW  - CEC 2015
N2  - 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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UR  - https://doi.org/10.28948/ngumuh.1570577
L1  - https://dergipark.org.tr/tr/download/article-file/4301853
ER  -