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

Yıl 2025, Cilt: 14 Sayı: 2, 597 - 606, 15.04.2025

Gurcan Yavuz , Hatem Dumlu , Yacoub Kadar Hassan

Öz

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.

Anahtar Kelimeler

Jaya, Local Search, β -Hill Climbing, CEC 2015

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

Öz

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.

Anahtar Kelimeler

Jaya, Yerel Arama, β-Hill Climbing, CEC 2015

Kaynakça

• A. Chaudhary and B. Bhushan, An improved teaching learning based optimization method to enrich the flight control of a helicopter system. Sādhanā, 48, 4, 222, 2023. https://doi.org/10.1007/s12046-023-02271-4
• Z. Chen, J. Zou, and W. Wang, Digital twin-oriented collaborative optimization of fuzzy flexible job shop scheduling under multiple uncertainties, Sādhanā. 48, 2, 78, 2023. https://doi.org/10.1007/s12046-023-02133-z
• M. Dehghani, Z. Montazeri, E. Trojovská, and P. Trojovský, Coati Optimization Algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems. Knowledge-Based Systems, 259, 110011, 2023. https://doi.org/10.1016/j.knosys.2022.110011
• A.H. Saadat, M. Fateh, and J. Keighobadi, Grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance. Turkish Journal of Electrical Engineering and Computer Sciences, 32, 126–143, 2024. https://doi.org/10.55730/1300-0632.4059
• E.H. Houssein, A.G. Gad, and Y.M. Wazery, Jaya Algorithm and Applications: A Comprehensive Review. in: N. Razmjooy, M. Ashourian, and Z. Foroozandeh, Eds. Metaheuristics and Optimization in Computer and Electrical Engineering, Springer International Publishing, pp. 3–24, Cham, 2021.
• R. Venkata Rao, Jaya: An Advanced Optimization Algorithm and its Engineering Applications, 2018.
• G. Yavuz, Senior Learning JAYA With Powell’s Method and Incremental Population Strategy. IEEE Access, 10, 103765–103780, 2022. https://doi.org/10.1109/ACCESS.2022.3210122
• H. M. Pandey, Jaya a novel optimization algorithm: What, how and why? 6th International Conference - Cloud System and Big Data Engineering (Confluence), 728-730, Noida, India, 2016, 14-15 January 2016.
• R.K. Achanta and V.K. Pamula, DC motor speed control using PID controller tuned by jaya optimization algorithm. 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), 983–987 , Chennai, India, 2017, 21-22 September 2017
• X. Jian and Z. Weng, A logistic chaotic JAYA algorithm for parameters identification of photovoltaic cell and module models. Optik, 203, 164041,2020. https://doi.org/10.1109/ACCESS.2022.3210122
• Y.-J. Zhang, Y.-F. Wang, L.-W. Tao, Y.-X. Yan, J. Zhao, and Z.-M. Gao, Self-adaptive classification learning hybrid JAYA and Rao-1 algorithm for large-scale numerical and engineering problems. Engineering Applications of Artificial Intelligence, 114, 105069, 2022. https://doi.org/10.1016/j.engappai.2022.105069
• F. Zhao, H. Zhang, L. Wang, et al., A surrogate-assisted Jaya algorithm based on optimal directional guidance and historical learning mechanism. Engineering Applications of Artificial Intelligence, 111, 104775, 2022. https://doi.org/10.1016/j.engappai.2022.104775
• D.R. Nayak, Y. Zhang, D.S. Das, and S. Panda, MJaya-ELM: A Jaya algorithm with mutation and extreme learning machine based approach for sensorineural hearing loss detection. Applied Soft Computing, 83, 105626, 2019. https://doi.org/10.1016/j.asoc.2019.105626
• M.A. Al-Betar, β-Hill climbing: an exploratory local search. Neural Computing and Applications, 28, 1, 153–168, 2017. https://doi.org/10.1007/s00521-016-2328-2
• K.K. Ghosh, S. Ahmed, P.K. Singh, Z.W. Geem, and R. Sarkar, Improved Binary Sailfish Optimizer Based on Adaptive β-Hill Climbing for Feature Selection. IEEE Access, 8, 83548–83560, 2020. https://doi.org/10.1109/ACCESS.2020.2991543
• B.H. Abed-alguni and F. Alkhateeb, Intelligent hybrid cuckoo search and β-hill climbing algorithm. Journal of King Saud University - Computer and Information Sciences, 32, 2, 159–173, 2020. https://doi.org/10.1016/j.jksuci.2018.05.003
• S. Ahmed, K.K. Ghosh, L. Garcia-Hernandez, A. Abraham, and R. Sarkar, Improved coral reefs optimization with adaptive β-hill climbing for feature selection. Neural Computing and Applications, 33, 12, 6467–6486, 2021. https://doi.org/10.1007/s00521-020-05409-1
• O.A. Alomari, A.T. Khader, M.A. Al-Betar, and M.A. Awadallah, A novel gene selection method using modified MRMR and hybrid bat-inspired algorithm with β-hill climbing. Applied Intelligence, 48, 11, 4429–4447, 2018. https://doi.org/10.1007/s10489-018-1207-1
• R. Rao, Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations,7, 1, 19–34, 2016.
• Z.A.A. Alyasseri, A.T. Khader, M.A. Al-Betar, and M.A. Awadallah, Hybridizing β-hill climbing with wavelet transform for denoising ECG signals. Information Sciences, 429, 229–246, 2018. https://doi.org/10.1016/j.ins.2017.11.026
• Q. Chen, B. Liu, Q. Zhang, J. Liang, P. Suganthan, and B. Qu, Problem definitions and evaluation criteria for CEC 2015 special session on bound constrained single-objective computationally expensive numerical optimization. Computational Intelligence Laboratory, Zhengzhou, China, Technical Report, Nov 2014.
• J.A. Nelder and R. Mead, A Simplex Method for Function Minimization. The Computer Journal, 7, 4, 308–313, 1965. https://doi.org/10.1093/comjnl/7.4.308
• D.F. Shanno, Conditioning of Quasi-Newton Methods for Function Minimization. Mathematics of Computation, 24, 111, 647–656, 1970. https://doi.org/10.2307/2004840
• W. Zhao, L. Wang, and Z. Zhang, Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283–304, 2019. https://doi.org/10.1016/j.knosys.2018.08.030
• A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872, 2019. https://doi.org/10.1016/j.future.2019.02.028
• F.A. Hashim, E.H. Houssein, M.S. Mabrouk, W. Al-Atabany, and S. Mirjalili, Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646–667, 2019. https://doi.org/10.1016/j.future.2019.07.015
• S.H. Samareh Moosavi and V.K. Bardsiri, Poor and rich optimization algorithm: A new human-based and multi populations algorithm. Engineering Applications of Artificial Intelligence, 86, 165–181, 2019. https://doi.org/10.1016/j.future.2019.07.015
• S. Arora and S. Singh, Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing, 23, 715–734, 2019. https://doi.org/10.1007/s00500-018-3102-4
• G. Dhiman and V. Kumar, Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159, 20–50, 2018. https://doi.org/10.1016/j.knosys.2018.06.001
• A. Baykasoğlu and Ş. Akpinar, Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems – Part 1: Unconstrained optimization. Applied Soft Computing, 56, 520–540, 2017. https://doi.org/10.1016/j.asoc.2015.10.036
• W.-T. Pan, A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26, 69–74, 2012. https://doi.org/10.1016/j.knosys.2011.07.001
• X. Yang and A. Hossein Gandomi, Bat algorithm: a novel approach for global engineering optimization. Engineering Computations, 29, 5, 464–483, 2012. https://doi.org/10.1108/02644401211235834
• X.-S. Yang, Firefly Algorithms for Multimodal Optimization. In: O. Watanabe and T. Zeugmann, Eds. Stochastic Algorithms: Foundations and Applications. Springer, Berlin, Heidelberg, pp. 169–178, 2009.
• E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, GSA: A Gravitational Search Algorithm. Information Sciences, 179, 13, 2232–2248, 2009. https://doi.org/10.1016/j.ins.2009.03.004
• P. Trojovský, M. Dehghani, and P. Hanuš, Siberian Tiger Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Engineering Optimization Problems. IEEE Access, 10, 132396–132431, 2022. https://doi.org/10.1109/ACCESS.2022.3229964