Improvement of L-shade Algorithm with Automatic Parameter Configuration Method

Year 2022, , 189 - 197, 30.04.2022

Gurcan Yavuz

https://doi.org/10.17671/gazibtd.1034921

Cited By: 2

Abstract

The L-shade algorithm, one of the important meta-heuristics of the last decade, is an adaptive DE variant. It has few control parameters and affects the execution of the algorithm. Determining these correctly has a critical role in algorithm performance. In this study, the control parameters of the L-shade algorithm are determined using irace, an automatic parameter configuration tool. The efficiency of the operation was tested using the CEC 2014 criteria set. The results obtained are compared with the predefined parameters of the L-shade and the parameters obtained by paramils, another parameter determination tool. The results of the experiment showed that better results were obtained with the parameter values obtained with the configuration tool. 

Keywords

l-shade, optimization, automatic parameter configuration

L-shade Algoritmasının Otomatik Parametre Yapılandırma Yöntemi ile İyileştirilmesi

Abstract

Son dönemin önemli metasezgisellerinden olan L-shade algoritması uyarlanabilir bir DE varyantıdır. Az sayıda kontrol parametresine sahiptir ve algoritmanın çalışmasını etkilemektedir. Bunların doğru şekilde belirlenmesi algoritma performansında kritik role sahiptir. Bu çalışmada, L-shade algoritmasına ait kontrol parametreleri bir otomatik parametre yapılandırma aracı olan irace kullanılarak belirlenmiştir. Yapılan işlemin etkinliği CEC 2014 ölçüt seti kullanılarak test edilmiştir. Elde edilen sonuçlar, L-shade’nin ön tanımlı parametreleri ve bir başka parametre belirleme aracı olan paramils’nin elde ettiği parametreler ile karşılaştırılmıştır. Deney sonuçları göstermiştir ki kullanılan yapılandırma aracı ile elde edilen parametre değerleri ile daha iyi sonuçlar elde edildiği görülmüştür.

Keywords

l-shade, optimizasyon, otomatik parametre yapılandırma

References

• B. M. Pant, H. Zaheer, L. Garcia-Hernandez, A. Abraham, “Differential Evolution: A review of more than two decades of research”, Eng. Appl. Artif. Intell., 90, February, 103479, 2020.
• T. Dokeroglu, E. Sevinc, T. Kucukyilmaz, A. Cosar, “A survey on new generation metaheuristic algorithms”, Comput. Ind. Eng., 137, August, 106040, 2019.
• S. A. UYMAZ, “Evaluation of the Most Valuable Player Algorithm for Solving Real-World Constrained Optimization Problems”, Bilişim Teknol. Derg., 14, 4, 345–353, 2021.
• G. Yavuz, “Diversified Position Update Equation-Based SSA with Refreshing-Gap Strategy for global optimization”, J. Comput. Sci., 60, 101597, 2022 doi: 10.1016/j.jocs.2022.101597.
• R. Storn, K. Price, “Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces”, Tech. report, Int. Comput. Sci. Inst., 11, 1–15, 1995, doi: 10.1023/A:1008202821328.
• D. H. Wolpert, W. G. Macready, “No free lunch theorems for optimization”, IEEE Trans. Evol. Comput., 1, 1, 67–82, Apr. 1997, doi: 10.1109/4235.585893.
• I. Boussaïd, J. Lepagnot, P. Siarry, “A survey on optimization metaheuristics”, Inf. Sci., 237, 82–117, 2013, doi: 10.1016/j.ins.2013.02.041.
• K. Hussain, M. N. Mohd Salleh, S. Cheng, Y. Shi, “Metaheuristic research: a comprehensive survey”, Artif. Intell. Rev., 52, 4, 2191–2233, 2019, doi: 10.1007/s10462-017-9605-z.
• S. Aslan, “Time-Based Dance Scheduling for Artificial Bee Colony Algorithm and Its Variants”, Int. J. Comput. Intell. Syst., 12, 2, 597, 2019, doi: 10.2991/ijcis.d.190425.001.
• S. Aslan, D. Karaboga, H. Badem, “A new artificial bee colony algorithm employing intelligent forager forwarding strategies”, Appl. Soft Comput., 96, 106656, 2020, doi: 10.1016/j.asoc.2020.106656.
• R. Tanabe, A. S. Fukunaga, “Improving the search performance of SHADE using linear population size reduction”, Proc. 2014 IEEE Congr. Evol. Comput. CEC 2014, 1658–1665, 2014, doi: 10.1109/CEC.2014.6900380.
• A. P. Piotrowski, J. J. Napiorkowski, “Step-by-step improvement of JADE and SHADE-based algorithms: Success or failure?”, Swarm Evol. Comput., 1–21, 2018, doi: 10.1016/J.SWEVO.2018.03.007.
• G. Yavuz, “Metasezgisel Algoritmaların Karşılaştırılmasında Kullanılan Ölçüt Setleri”, Dijital Mühendislik, Iksad Publications, TR, 29–59, 2020.
• R. Tanabe, A. Fukunaga, “Reviewing and Benchmarking Parameter Control Methods in Differential Evolution,” IEEE Trans. Cybern., 50, 3, 1170–1184, 2020, doi: 10.1109/TCYB.2019.2892735.
• M. A. Elhosseini, R. A. El Sehiemy, Y. I. Rashwan, X. Z. Gao, “On the performance improvement of elephant herding optimization algorithm”, Knowledge-Based Syst., 166, 58–70, 2019, doi: 10.1016/j.knosys.2018.12.012.
• T. Liao, M. A. M. de Oca, T. Stützle, “Computational results for an automatically tuned CMA-ES with increasing population size on the CEC’05 benchmark set”, Soft Comput., 17, 6, 1031–1046, 2013, doi: 10.1007/s00500-012-0946-x.
• T. Liao, D. Molina, T. Stützle, “Performance evaluation of automatically tuned continuous optimizers on different benchmark sets”, Appl. Soft Comput. J., 27, 490–503, 2015, doi: 10.1016/j.asoc.2014.11.006.
• D. Aydın, G. Yavuz, T. Stützle, “ABC-X: a generalized, automatically configurable artificial bee colony framework”, Swarm Intell., 11, 1, 1–38, 2017, doi: 10.1007/s11721-017-0131-z.
• J. J. Liang, B. Y. Qu, P. N. Suganthan, “Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization”, Comput. Intell. Lab., 2013.
• A. P. Piotrowski, “L-SHADE optimization algorithms with population-wide inertia”, Inf. Sci., 468, 117–141, 2018, doi: 10.1016/j.ins.2018.08.030.
• C. Huang, Y. Li, X. Yao, “A Survey of Automatic Parameter Tuning Methods for Metaheuristics”, IEEE Trans. Evol. Comput., 24, 2, 201–216, 2020, doi: 10.1109/TEVC.2019.2921598.
• F. Hutter, H. H. Hoos, K. Leyton-Brown, T. Stützle, “ParamILS: An automatic algorithm configuration framework”, J. Artif. Intell. Res., 36, 1, 267–306, 2009, doi: 10.1613/jair.2808.
• C. Ansótegui, M. Sellmann, K. Tierney, “A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms”, International Conference on Principles and Practice of Constraint Programming, Berlin, 2009.
• F. Hutter, H. H. Hoos, K. Leyton-Brown, “Sequential Model-Based Optimization for General Algorithm Configuration”, Lion-5, 507–523, 2011.
• M. Birattari, Z. Yuan, P. Balaprakash, T. Stützle, “F-Race and Iterated F-Race: An Overview”, Experimental Methods for the Analysis of Optimization Algorithms, Springer, Berlin, 311–336, 2010.
• M. López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, M. Birattari, T. Stützle, “The irace package: Iterated racing for automatic algorithm configuration”, Oper. Res. Perspect., 3, 43–58, 2016, doi: 10.1016/j.orp.2016.09.002.