Gerçek Dünya Kısıtlı Optimizasyon Problemlerinin Çözümü için En Değerli Oyuncu Algoritmasının Değerlendirilmesi

Year 2021, Volume: 14 Issue: 4, 345 - 353, 31.10.2021

Sait Ali Uymaz

Abstract

Gerçek-dünya kısıtlı optimizasyon problemlerinin, karar değişkenlerine ek olarak kısıtlamaları ve yerel minimum noktaları vardır. Kısıtlamalar nedeniyle bu problemlerin arama alanları çok küçük olduğu için çözülmesi zor ve zaman alıcıdır. Son yıllarda, bu tür problemleri çözmek için birçok yeni meta-sezgisel algoritma önerilmiş ve kısıt işleme teknikleriyle birleştirilmiştir. Spor etkinliklerinden esinlenerek yakın zamanda önerilen bir meta-sezgisel optimizasyon algoritması olan En Değerli Oyuncu Algoritması (MVPA), matematiksel test fonksiyonları üzerinde test edilmiştir. Bu çalışmada, MVPA algoritması kısıt işleme teknikleri ve bazı modifikasyonlar ile birleştirilerek 19 kısıtlı gerçek dünya mühendislik optimizasyon problemi üzerinde test edilmiştir. Sonuçlar, kısıtları sağlayan uygun çözümler bulmada yüksek bir başarı oranı göstermiştir. 

Keywords

Kısıt işleme tekniği, Mühendislik problemleri, en değerli oyuncu algoritması, Metasezgisel

Evaluation of the Most Valuable Player Algorithm for Solving Real-World Constrained Optimization Problems

Abstract

Real-world constrained optimization problems have constraints and local minimums in addition to decision variables. They are time consuming and difficult to solve since the search spaces of these problems are very small due to the constraints. In recent years, many new metaheuristic algorithms have been proposed and combined with constraint handling techniques to solve such problems. The most valuable player algorithm (MVPA), a recently proposed metaheuristic optimization algorithm, inspired by sports events, has been tested on mathematical benchmark functions. In this study, the MVPA algorithm is combined with constraint handling techniques and some modifications and tested on 19 real-world constrained engineering optimization problems. The results showed a high success rate in finding feasible solutions.

Keywords

Constraint handling technique, Engineering problems, The most valuable player algorithm, Metaheuristic

References

• R. Mallipeddi and P. N. Suganthan, "Ensemble of Constraint Handling Techniques", IEEE Transactions on Evolutionary Computation, 14(4), 561-579, 2010.
• E. Mezura-Montes and C. A. C. Coello, "Constraint-handling in nature-inspired numerical optimization: Past, present and future", Swarm and Evolutionary Computation, 1(4), 173-194, 2011.
• Z. Michalewicz, “A survey of constraint handling techniques in evolutionary computation methods”, Evolutionary Programming, 4, 135-155, 1995.
• A. Kumar, G. H. Wu, M. Z. Ali, R. Mallipeddi, P. N. Suganthan, and S. Das, "A test-suite of non-convex constrained optimization problems from the real-world and some baseline results", Swarm and Evolutionary Computation, 56, 2020, doi: 10.1016/j.swevo.2020.100693.
• C. Y. Si, J. J. Hu, T. Lan, L. Wang, and Q. D. Wu, "A combined constraint handling framework: an empirical study", Memetic Computing, 9(1), 69-88, 2017.
• Z. Michalewicz and M. Schoenauer, "Evolutionary Algorithms for Constrained Parameter Optimization Problems", Evolutionary computation, 4(1), 1-32, 1996.
• C. A. C. Coello, "Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art", Computer methods in applied mechanics and engineering, 191(11-12), 1245-1287, 2002.
• J. J. Liu, S. H. Zhang, C. Z. Wu, J. W. Liang, X. Y. Wang, and K. L. Teo, "A hybrid approach to constrained global optimization", Applied Soft Computing, 47, 281-294, 2016.
• B. Tessema and G. G. Yen, "A self adaptive penalty function based algorithm for constrained optimization", 2006 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada, 246-253, 2006.
• Z. J. Liu, B. Q. Lu, and Y. Cao, "A new optimization method based on restructuring penalty function for solving constrained minimization problems", 2006 IEEE International Conference on Granular Computing, Atlanta, USA, 510-514, 2006.
• B. Tessema and G. G. Yen, "An Adaptive Penalty Formulation for Constrained Evolutionary Optimization", IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 39(3), 565-578, 2009.
• A. Cinar and M. Kiran, "The Performance of Penalty Methods on Tree-Seed Algorithm for Numerical Constrained Optimization Problems", International Arab Journal of Information Technology, 17(5), 799-807, 2020.
• K. M. Ang, W. H. Lim, N. A. M. Isa, S. S. Tiang, and C. H. Wong, "A constrained multi-swarm particle swarm optimization without velocity for constrained optimization problems", Expert Systems with Applications, 140, 2020, doi: 10.1016/j.eswa.2019.112882.
• K. Gupta, K. Deep, and J. C. Bansal, "Spider monkey optimization algorithm for constrained optimization problems", Soft Computing, 21(23), 6933-6962, 2017.
• D. Karaboga and B. Akay, "A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems", Applied Soft Computing, 11(3), 3021-3031, 2011.
• M. Kohli and S. Arora, "Chaotic grey wolf optimization algorithm for constrained optimization problems", Journal of Computational Design and Engineering, 5(4), 458-472, 2018.
• M. G. H. Omran and A. Salman, "Constrained optimization using CODEQ", Chaos, Solitons & Fractals, 42(2), 662-668, 2009.
• A. Trivedi, K. Sanyal, P. Verma, and D. Srinivasan, "A Unified Differential Evolution Algorithm for Constrained Optimization Problems", 2017 IEEE Congress on Evolutionary Computation (CEC), San Sebastián, Spain, 1231-1238, 2017.
• Ü. Atila, M. Dorterler, and İ. Şahin, “Yapay Alg Algoritmasının Tasarım Optimizasyon Problemlerindeki Performansı Üzerine Bir Çalışma: Basınç Yayı Örneği”, Bilişim Teknolojileri Dergisi, 11(4), 349-355, 2018, doi:10.17671/gazibtd.452992.
• H. R. E. H. Bouchekara, "Most Valuable Player Algorithm: a novel optimization algorithm inspired from sport", Operational Research, 20(1), 139-195, 2020.
• X. Liu, Q. F. Luo, D. Y. Wang, M. Abdel-Baset, and S. Q. Jiang, "An Improved Most Valuable Player Algorithm with Twice Training Mechanism", In International Conference on Intelligent Computing, Springer, Cham, 854-865, 2018.
• M. A. M. Ramli and H. R. E. H. Bouchekara, "Wind Farm Layout Optimization Considering Obstacles Using a Binary Most Valuable Player Algorithm", Ieee Access, 8, 131553-131564, 2020.
• K. Srilakshmi, P. R. Babu, and P. Aravindhababu, "An enhanced most valuable player algorithm based optimal power flow using Broyden's method", Sustainable Energy Technologies and Assessments, 42, 2020, doi: 10.1016/j.seta.2020.100801.
• A. Korashy, S. Kamel, A. R. Youssef, and F. Jurado, "Most Valuable Player Algorithm for Solving Direction Overcurrent Relays Coordination Problem", Proceedings of 2019 International Conference on Innovative Trends in Computer Engineering (Itce 2019), Aswan, Egypt, 466-471, 2019.
• S. M. Elsayed, R. A. Sarker, and D. L. Essam, "A new genetic algorithm for solving optimization problems", Engineering Applications of Artificial Intelligence, 27, 57-69, 2014.
• C. A. C. Coello, "Use of a self-adaptive penalty approach for engineering optimization problems", Computers in Industry, 41(2), 113-127, 2000.
• Z. Yan, J. Wang, and G. C. Li, "A collective neurodynamic optimization approach to bound-constrained nonconvex optimization", Neural Networks, 55, 20-29, 2014.