Time-varying Random Inertia Weighted Jaya Algorithm for the Solution of Global Optimization Problems

Yıl 2022, , 123 - 135, 01.03.2022

Mehmet Fatih Tefek

https://doi.org/10.2339/politeknik.745819

Cited By: 2

Öz

Anahtar Kelimeler

Jaya, inertia weight, optimization, global optimization problems

Küresel Optimizasyon Problemlerinin Çözümü İçin Zamanla Değişen Rastgele Atalet Ağırlıklı Jaya Algoritması

Öz

Jaya algoritması küresel optimizasyon problemlerini çözmek için son zamanlarda sıklıkla kullanılan popülasyon tabanlı bir optimizasyon algoritmasıdır. Bu çalışmada küresel optimizasyon problemlerinin çözümü için zamanla değişen rastgele atalet ağırlıklı Jaya (ZR-Jaya) algoritması geliştirilmiştir. Geliştirilen algoritmada Jaya’ya göre optimizasyon problemlerini daha erken iterasyonlarda çözmek, yakınsama süresini azaltmak ve daha iyi çözüm elde etmek amaçlanmıştır. ZR-Jaya deneysel çalışmalar için literatürde iyi bilinen on adet kıyaslama fonksiyonu ile bu fonksiyonların birleşiminden oluşan beş adet kompozit küresel optimizasyon problemlerine uygulanmıştır. ZR-Jaya algoritmasının bulduğu sonuçlar Yapay Arı Kolonisi (YAK), Parçacık Sürü Optimizasyon (PSO), Jaya algoritmaları ve Jaya’nın güncelleme prosedürüne eklenen rastgele atalet ağırlıklı Jaya (RAA-Jaya), doğrusal azalan atalet ağırlıklı Jaya (DAAA-Jaya) ve karmaşık atalet ağırlıklı Jaya (KAA-Jaya) ile karşılaştırılmıştır. Geliştirilen algoritmanın başarısı YAK, PSO, Jaya ve Jaya’nın diğer ağırlık stratejileriyle kıyaslanmış ve sonuçlar çizelgelerde verilmiş ve grafiklerle gösterilmiştir. Deneysel çalışma sonuçlarına göre ZR-Jaya’nın PSO, YAK, Jaya ve Jaya’nın diğer ağırlık stratejilerinden, tek-yerel noktalı fonksiyonlarda başarı performans sayısı oranı %75, çok-yerel noktalı fonksiyonlarda ise %61,11 olmuştur. Geliştirilen ZR-Jaya algoritmasında zamanla değişen rastgele atalet ağırlığı faktörünün oldukça etkili olduğu ve uygulanabilir olduğu deneysel çalışmalarla tespit edilmiştir.

Anahtar Kelimeler

Jaya, atalet ağırlığı, optimizasyon, küresel optimizasyon problemleri

Kaynakça

• [1] Yılmaz S. and Küçüksille E. U., "A new modification approach on bat algorithm for solving optimization problems", Applied Soft Computing, 28: 259-275, (2015).
• [2] Kennedy J. and Eberhart R., "Particle swarm optimization", Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, 4: 1942-1948, (1995).
• [3] Karaboga D. and Akay B., "A Survey: Algorithms Simulating Bee Swarm Intelligence”, Artificial Intelligence Review, 31: 68-85, (2009).
• [4] Venkata R. R. , "Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems", International Journal of Industrial Engineering Computations, 7: 19-34, (2016).
• [5] Venkata R. R. and Saroj A., "A self-adaptive multi-population based Jaya algorithm for engineering optimization", Swarm and Evolutionary Computation, 37: 1-26, (2017).
• [6] Bhoye M., Pandya M. H., Valvi S., Trivedi I. N., Jangir P., and Parmar S. A., "An emission constraint Economic Load Dispatch problem solution with Microgrid using JAYA algorithm", 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS), Nagercoil, 497-502, (2016).
• [7] Huang C., Wang L., Yeung R. S., Zhang Z., Chung H. S., and Bensoussan A., "A Prediction Model-Guided Jaya Algorithm for the PV System Maximum Power Point Tracking", IEEE Transactions on Sustainable Energy, 9 (1): 45-55, (2018).
• [8] Gao K., Zhang Y., Sadollah A., Lentzakis A., and Su R., "Jaya, harmony search and water cycle algorithms for solving large-scale real-life urban traffic light scheduling problem", Swarm and Evolutionary Computation, 37: 58-72, (2017).
• [9] Öztürk H. T. and Türkeli E., "Tabanında Anahtar Kesiti Bulunan Betonarme İstinat Duvarlarının Jaya Algoritmasıyla Optimum Tasarımı", Politeknik Dergisi, 22 (2) 2147-9429, (2019).
• [10] Gao K., Sadollah A., Zhang Y., Su R., and Li K. G. J., "Discrete Jaya algorithm for flexible job shop scheduling problem with new job insertion", 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, 1-5, (2016).
• [11] Wang L. and Huang C., "A novel Elite Opposition-based Jaya algorithm for parameter estimation of photovoltaic cell models", Optik, 155: 351-356, (2018).
• [12] Migallón H., Jimeno-Morenilla A., Sánchez-Romero J. L., and Belazi A., "Efficient parallel and fast convergence chaotic Jaya algorithms", Swarm and Evolutionary Computation, 56: 1-17, (2020).
• [13] Xin J., Chen G., and Hai Y., "A Particle Swarm Optimizer with Multi-stage Linearly-Decreasing Inertia Weight", International Joint Conference on Computational Sciences and Optimization, Sanyan, 505-508, (2009).
• [14] Bansal J. C., Singh P. K., Saraswat M., Verma A., Jadon S. S., and Abraham A., "Inertia Weight strategies in Particle Swarm Optimization", Third World Congress on Nature and Biologically Inspired Computing, Salamanca, 633-640, (2011).
• [15] Eberhart R. C. and Yuhui S., "Tracking and optimizing dynamic systems with particle swarms", Proceedings of the 2001 Congress on Evolutionary Computation, Seul, 94-100, (2001).
• [16] Feng Y., Teng G., Wang A., and Yao Y., "Chaotic Inertia Weight in Particle Swarm Optimization", Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007), Kumamato, 475-475, (2007).
• [17] Shi Y. and Eberhart R., "A modified particle swarm optimizer", IEEE World Congress on Computational Intelligence, Anchorage, 69-73, (1998).
• [18] Arumugam M. S. and Rao M. V. C., "On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems", Applied Soft Computing, 8 (1): 324-336, (2008).
• [19] Nickabadi A., Ebadzadeh M. M., and Safabakhsh R., "A novel particle swarm optimization algorithm with adaptive inertia weight", Applied Soft Computing, 11 (4): 3658-3670, (2011).
• [20] Fan S.K. S. and Chiu Y.Y., "A decreasing inertia weight particle swarm optimizer", Engineering Optimization,39 (2): 203-228, (2007).
• [21] Alatas B., Akin E., and Ozer A., “Kaotik Haritalı Parçacık Sürü Optimizasyon Algoritmaları”, XII. Elektrik Elektronik Bilgisayar Biyomedikal Mühendisliği Ulusal Kongresi, Eskişehir, (2007).
• [22] Aydilek İ. B., "An Ensemble inertia Weight Calculation Strategy in Particle Swarm Optimization Algorithm", Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi (SUJEST), 6 (4): 544-558, (2018).
• [23] Rauf H. T., Malik S., Shoaib U., Irfan M. N., and Lali M. I., "Adaptive inertia weight Bat algorithm with Sugeno-Function fuzzy search", Applied Soft Computing,90:106159, (2020).
• [24] Yue X. and Zhang H., "Modified hybrid bat algorithm with genetic crossover operation and smart inertia weight for multilevel image segmentation", Applied Soft Computing, 90: 106157, (2020).
• [25] Ekinci S., Hekimoğlu B., Demirören A., and Eker E., "Speed Control of DC Motor Using Improved Sine Cosine Algorithm Based PID Controller", 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, 1-7, (2019).
• [26] Ramli M. R., Abas Z. A., Desa M. I., Abidin Z. Z., and Alazzam M. B., "Enhanced convergence of Bat Algorithm based on dimensional and inertia weight factor", Journal of King Saud University - Computer and Information Sciences, 31 (4): 452-458, (2019).
• [27] Gan C., Cao W., Wu M., and Chen X., "A new bat algorithm based on iterative local search and stochastic inertia weight", Expert Systems with Applications, 104: 202-212, (2018).
• [28] Yılmaz S. and Kucuksille E. U., “Improved Bat Algorithm (IBA) on Continuous Optimization Problems”, Lecture Notes on Software Engineering, 1 (3): 279-283, 2013.
• [29] Toz G., Yücedağ İ., and Erdoğmuş P., "A fuzzy image clustering method based on an improved backtracking search optimization algorithm with an inertia weight parameter," Journal of King Saud University - Computer and Information Sciences, 31 (3): 295-303, (2019).
• [30] Wu Z.S., Fu W.P., and Xue R., "Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem", Computational Intelligence and Neuroscience, 2015: 1-15, (2015).
• [31] Shukla A. K., Singh P., and Vardhan M., "An adaptive inertia weight teaching-learning-based optimization algorithm and its applications", Applied Mathematical Modelling, 77: 309-326, (2020).
• [32] Suganthan P. N., Hansen N., Liang J. J., Deb K., Chen Y.P., Auger A., Tiwari S.., "Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization", Natural Computing, 341-357, (2005).
• [33] Kıran M. S. and Gündüz M., "A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems", Applied Soft Computing, 13(4):2188-2203, (2013).
• [34] Wang D., Tan D., and Liu L., "Particle swarm optimization algorithm: an overview", Soft Computing, 22 (2): 387-408, (2018).
• [35] Shi Y. and Eberhart R. C., "Empirical study of particle swarm optimization", Proceedings of the 1999 Congress on Evolutionary Computation, Washington, 3:1945-1950, (1999).
• [36] Karaboga D. and Akay B., "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm", Journal of Global Optimization, 39 (3): 459-471,(2007).