A NOVEL APPROACH BASED ON ELEPHANT HERDING OPTIMIZATION FOR CONSTRAINED OPTIMIZATION PROBLEMS

Öz

Many real-world problems can be formulated as an optimization problem and they have some constraints generally. To overcome these constraints, bio-inspired algorithms are adapted to constrained optimization using constraint handling methods and some modifications. In this study, a new approach is developed to solve constrained optimization problems with elephant herding optimization algorithm which is a newly-emerging optimization technique. Besides the basic EHO, two EHO variants (EHO-NoB and GL-EHO) are adapted to constrained optimization with this approach. The well-known thirteen constrained benchmark functions are used to analysis the performances of algorithms. Experimental results show that the GL-EHO has a better performance than the basic EHO and other algorithms. In addition, the results of GL-EHO are comparable level with the result of another EHO variant in the literature.

Anahtar Kelimeler

• Constrained optimization,Deb’s rules,Elephant herding optimization

Kısıtlı Optimizasyon Problemleri için Fil Sürüsü Optimizasyonu Tabanlı Yeni Bir Yaklaşım

Öz

Birçok gerçek dünya problemi bir optimizasyon problemi olarak formüle edilebilir ve genel olarak bazı kısıtlamalara sahiptirler. Bu kısıtlamaların üstesinden gelmek için, kısıtlama yöntemleri ve bazı modifikasyonlar kullanarak doğa esinli algoritmalar kısıtlı optimizasyona uyarlanmıştır. Bu çalışmada, yeni ortaya çıkan bir optimizasyon tekniği olan fil sürü optimizasyonu algoritması ile kısıtlı optimizasyon problemlerini çözmek için yeni bir yaklaşım geliştirilmiştir. Temel EHO'nun yanı sıra, iki EHO varyantı (EHO-NoB ve GL-EHO) bu yaklaşımla kısıtlı optimizasyona uyarlanmıştır. İyi bilinen on üç kısıtlı test fonksiyonu, algoritmaların performanslarını analiz etmek için kullanılmıştır. Deneysel sonuçlar, GL-EHO'nun temel EHO ve diğer algoritmalardan daha iyi bir performansa sahip olduğunu göstermektedir. Ayrıca, GL-EHO sonuçları literatürdeki başka bir EHO varyantının sonucuyla karşılaştırılabilir düzeydedir.

Anahtar Kelimeler

• Deb kuralları,Fil sürü optimizasyonu,Kısıtlı optimizasyon

Kaynakça

1. Alihodzic, A., Tuba, E., Capor-Hrosik, R., Dolicanin, E., Tuba, M., 2017, "Unmanned Aerial Vehicle Path Planning Problem by Adjusted Elephant Herding Optimization", 2017 25th Telecommunication Forum (Telfor), 804-807.
2. Asafuddoula, M., Ray, T., Sarker, R., 2014, "An adaptive hybrid differential evolution algorithm for single objective optimization", Applied Mathematics and Computation, 231, 601-618. doi:10.1016/j.amc.2014.01.041
3. Babalik, A., Cinar, A. C., Kiran, M. S., 2018, "A modification of tree-seed algorithm using Deb's rules for constrained optimization", Applied Soft Computing, 63, 289-305. doi:10.1016/j.asoc.2017.10.013
4. Deb, K., 2000, "An efficient constraint handling method for genetic algorithms", Computer Methods in Applied Mechanics and Engineering, 186(2-4), 311-338. doi:Doi 10.1016/S0045-7825(99)00389-8
5. Farnad, B., Jafarian, A., Baleanu, D., 2018, "A new hybrid algorithm for continuous optimization problem", Applied Mathematical Modelling, 55, 652-673. doi:10.1016/j.apm.2017.10.001
6. Garg, H., 2016, "A hybrid PSO-GA algorithm for constrained optimization problems", Applied Mathematics and Computation, 274, 292-305. doi:10.1016/j.amc.2015.11.001
7. Hakli, H., "An improved elephant herding optimization by balancing local and global search for continuous optimization", 15th International Conference on Informatics and Information Technologies, CIIT 2018, Mavrovo, Macedonia. In Press. 2018.
8. Hakli, H., Uguz, H., 2017, "A novel approach for automated land partitioning using genetic algorithm", Expert Systems with Applications, 82, 10-18. doi:10.1016/j.eswa.2017.03.067

9. Jiao, R. W., Zeng, S. Y., Alkasassbeh, J. S., Li, C. H., 2017, "Dynamic multi-objective evolutionary algorithms for single-objective optimization", Applied Soft Computing, 61, 793-805. doi:10.1016/j.asoc.2017.08.030
10. Karaboga, D., (2005). An idea based on honey bee swarm for numerical optimization. Retrieved from Technical Report-TR06, Erciyes University, Engineering Faculty, Comput. Eng.Dep.:
11. Kennedy, J., Eberhart, R., "Particle swarm optimization", Sixth International Symposium on Micro Machine and Human Science, Nagoya,Japan. 39–43. 1995.
12. Kiran, M. S., 2015, "TSA: Tree-seed algorithm for continuous optimization", Expert Systems with Applications, 42(19), 6686-6698. doi:10.1016/j.eswa.2015.04.055
13. Kohli, M., Arora, S., 2017, "Chaotic grey wolf optimization algorithm for constrained optimization problems", Journal of Computational Design and Engin eering, In Press. doi:10.1016/j.jcde.2017.02.005
14. Koziel, S., Michalewicz, Z., 1999, "Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization", Evolutionary Computation, 7(1), 19-44. doi:DOI 10.1162/evco.1999.7.1.19
15. Lin, C. H., 2013, "A rough penalty genetic algorithm for constrained optimization", Information Sciences, 241, 119-137. doi:10.1016/j.ins.2013.04.001
16. Luo, J. P., Yang, Y., Liu, Q. Q., Li, X., Chen, M. R., Gao, K. Z., 2018, "A new hybrid memetic multi-objective optimization algorithm for multi-objective optimization", Information Sciences, 448, 164-186. doi:10.1016/j.ins.2018.03.012
17. Meena, N. K., Parashar, S., Swarnkar, A., Gupta, N., Niazi, K. R., 2018, "Improved Elephant Herding Optimization for Multiobjective DER Accommodation in Distribution Systems", Ieee Transactions on Industrial Informatics, 14(3), 1029-1039. doi:10.1109/Tii.2017.2748220
18. Mezura-Montes, E., Coello, C. A. C., 2011, "Constraint-handling in nature-inspired numerical optimization: Past, present and future", Swarm and Evolutionary Computation, 1(4), 173-194. doi:10.1016/j.swevo.2011.10.001
19. Niu, B., Wang, J. W., Wang, H., 2015, "Bacterial-inspired algorithms for solving constrained optimization problems", Neurocomputing, 148, 54-62. doi:10.1016/j.neucom.2012.07.064
20. Parashar, S., Swarnkar, A., Niazi, K. R., Gupta, N., 2017, "A Modified Elephant Herding Optimization For Economic Generation Co-Ordination Of DERs And BESS In Grid Connected Microgrid", Journal of Engineering-Joe.
21. Runarsson, T. P., Yao, X., 2000, "Stochastic ranking for constrained evolutionary optimization", Ieee Transactions on Evolutionary Computation, 4(3), 284-294. doi:Doi 10.1109/4235.873238
22. Sambariya, D. K., Fagna, R., 2017, "A novel Elephant Herding Optimization based PID controller design for Load Frequency Control in Power System", 2017 International Conference on Computer, Communications and Electronics (Comptelix), 595-600.
23. Sharma, A., Kumar, R., Panigrahi, B. K., Das, S., 2017, "Termite spatial correlation based particle swarm optimization for unconstrained optimization", Swarm and Evolutionary Computation, 33, 93-107. doi:10.1016/j.swevo.2016.11.001
24. Strumberger, I., Bacanin, N., Tomic, S., Beko, M., Tuba, M., 2017, "Static Drone Placement by Elephant Herding Optimization Algorithm", 2017 25th Telecommunication Forum (Telfor), 808-811.
25. Strumberger, I., Bacanin, N., Tuba, M., "Hybridized Elephant Herding Optimization Algorithm for Constrained Optimization", Cham. 158-166. 2018.
26. Tuba, E., Alihodzic, A., Tuba, M., 2017, "Multilevel Image Thresholding Using Elephant Herding Optimization Algorithm", 2017 14th International Conference on Engineering of Modern Electric Systems (Emes), 240-243.
27. Wang, B.-C., Li, H.-X., Feng, Y., 2018, "An improved teaching-learning-based optimization for constrained evolutionary optimization", Information Sciences, 456, 131–144. Wang, G. G., Deb, S., Coelho, L. D., 2015, "Elephant Herding Optimization", 2015 3rd International Symposium on Computational and Business Intelligence (Iscbi 2015), 1-5. doi:10.1109/Iscbi.2015.8
28. Xu, B., Chen, X., Tao, L. L., 2018, "Differential evolution with adaptive trial vector generation strategy and cluster-replacement-based feasibility rule for constrained optimization", Information Sciences, 435, 240-262. doi:10.1016/j.ins.2018.01.014