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Elephant Herding Optimization Using Multi-Search Strategy for Continuous Optimization Problems

Year 2019, Volume: 7 Issue: 2, 261 - 268, 25.05.2019
https://doi.org/10.21541/apjes.455717

Abstract

The elephant herding optimization (EHO), which imitates social behaviors of the elephants, is recently proposed a swarm
intelligence and population-based optimization algorithm. Although EHO is a good at local search, it is not effective on the
global search due to the rapid loss of population diversity. In the basic EHO method, a single solution search equation is used
for the generating the new individuals. Therefore, it is insufficient on the solving the problems which have different
characteristics and the exploring the search space effectively. In this study, in order to overcome these problems and to provide
a balance between exploration and exploitation, elephant herding optimization using multi-search strategy (Multi-EHO) has been
proposed which inspired by the search strategies of the most well-known optimization techniques. For the comparison of the
proposed method and the basic EHO, the CEC2015 benchmark set with 15 different functions is used. In addition, to validate
the performance of Multi-EHO, the proposed method is compared with the grey wolf optimizer (GWO) and the whale
optimization algorithm (WOA) proposed in recent years. Experimental results show that the proposed method has more
successful and more robust performance than other methods.-

References

  • [1] H. Hakli and H. Uguz, "A novel particle swarm optimization algorithm with Levy flight," Applied Soft Computing, vol. 23, pp. 333-345, Oct 2014.
  • [2] I. Strumberger, N. Bacanin, and M. Tuba, "Hybridized Elephant Herding Optimization Algorithm for Constrained Optimization," Cham, 2018, pp. 158-166.
  • [3] D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Technical Report-TR06, Erciyes University, Engineering Faculty, Comput. Eng.Dep.2005.
  • [4] J. Kennedy and R. Eberhart, "Particle swarm optimization," presented at the Sixth International Symposium on Micro Machine and Human Science, Nagoya,Japan, 1995.
  • [5] M. Dorigo and G. D. Caro, "Ant colony optimization: a new meta-heuristic," presented at the Proceedings of the 1999 Congress on Evolutionary Computation, Washington,DC., 1999.
  • [6] S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey Wolf Optimizer," Advances in Engineering Software, vol. 69, pp. 46-61, Mar 2014.
  • [7] S. Mirjalili and A. Lewis, "The Whale Optimization Algorithm," Advances in Engineering Software, vol. 95, pp. 51-67, May 2016.
  • [8] G. G. Wang, S. Deb, and L. D. Coelho, "Elephant Herding Optimization," 2015 3rd International Symposium on Computational and Business Intelligence (Iscbi 2015), pp. 1-5, 2015.
  • [9] I. Strumberger, N. Bacanin, S. Tomic, M. Beko, and M. Tuba, "Static Drone Placement by Elephant Herding Optimization Algorithm," 2017 25th Telecommunication Forum (Telfor), pp. 808-811, 2017.
  • [10] E. Tuba, A. Alihodzic, and M. Tuba, "Multilevel Image Thresholding Using Elephant Herding Optimization Algorithm," 2017 14th International Conference on Engineering of Modern Electric Systems (Emes), pp. 240-243, 2017.
  • [11] E. Tuba and Z. Stanimirovic, "Elephant Herding Optimization Algorithm for Support Vector Machine Parameters Tuning," Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence - Ecai 2017, 2017.
  • [12] A. Alihodzic, E. Tuba, R. Capor-Hrosik, E. Dolicanin, and M. Tuba, "Unmanned Aerial Vehicle Path Planning Problem by Adjusted Elephant Herding Optimization," 2017 25th Telecommunication Forum (Telfor), pp. 804-807, 2017.
  • [13] M. A. Sarwar, B. Amin, N. Ayub, S. H. Faraz, S. U. R. Khan, and N. Javaid, "Scheduling of Appliances in Home Energy Management System Using Elephant Herding Optimization and Enhanced Differential Evolution," Advances in Intelligent Networking and Collaborative Systems, Incos-2017, vol. 8, pp. 132-142, 2018.
  • [14] D. K. Sambariya and R. Fagna, "A Robust PID Controller for Load Frequency Control of Single Area Re-heat Thermal Power Plant using Elephant Herding Optimization Techniques," 2017 Ieee International Conference on Information, Communication, Instrumentation and Control (Icicic), 2017.
  • [15] V. Tuba, M. Beko, and M. Tuba, "Performance of Elephant Herding Optimization Algorithm on CEC 2013 real parameter single objective optimization," WSEAS TRANSACTIONS on SYSTEMS, vol. 16, pp. 100-105, 2017.
  • [16] S. Parashar, A. Swarnkar, K. R. Niazi, and N. Gupta, "A modified elephant herding optimization for economic generation co-ordination of DERs and BESS in grid connected microgrid," Journal of Engineering-Joe, Nov 15 2017.
  • [17] N. K. Meena, S. Parashar, A. Swarnkar, N. Gupta, and K. R. Niazi, "Improved Elephant Herding Optimization for Multiobjective DER Accommodation in Distribution Systems," IEEE Transactions on Industrial Informatics, vol. PP, 2017.
  • [18] E. Tuba, R. Capor-Hrosik, A. Alihodzic, R. Jovanovic, and M. Tuba, "Chaotic Elephant Herding Optimization Algorithm," presented at the IEEE 16th World Symposium on Applied Machine Intelligence and Informatics, Kosice,Slovakia, 2018.
  • [19] R. Storn and K. Price, "Differential evolution-A simple and efficient adaptive scheme for global optimization over continuous spaces," Berkeley: ICSI, 1995.
  • [20] G. G. Wang, S. Deb, X. Z. Gao, and L. D. Coelho, "A new metaheuristic optimisation algorithm motivated by elephant herding behaviour," International Journal of Bio-Inspired Computation, vol. 8, pp. 394-409, 2016.
  • [21] H. Wang, Z. J. Wu, S. Rahnamayan, H. Sun, Y. Liu, and J. S. Pan, "Multi-strategy ensemble artificial bee colony algorithm," Information Sciences, vol. 279, pp. 587-603, Sep 20 2014.
  • [22] M. S. Kiran, H. Hakli, M. Gunduz, and H. Uguz, "Artificial bee colony algorithm with variable search strategy for continuous optimization," Information Sciences, vol. 300, pp. 140-157, Apr 10 2015.
  • [23] H. Hakli, "A modified cuckoo search using different search strategies," International Journal of Intelligent Systems and Applications in Engineering, vol. 4 (Special Issue), pp. 190-194, 2016.
  • [24] Y. Wang, B. Li, T. Weise, J. Y. Wang, B. Yuan, and Q. J. Tian, "Self-adaptive learning based particle swarm optimization," Information Sciences, vol. 181, pp. 4515-4538, Oct 15 2011.
  • [25] H. Hakli, "An improved elephant herding optimization by balancing local and global search for continuous optimization," presented at the 15th International Conference on Informatics and Information Technologies, CIIT 2018, Mavrovo, Macedonia, 2018.
  • [26] J. J. Liang, B. Y. Qu, P. N. Suganthan, and Q. Chen, "Problem Definitions and Evaluation Criteria for the CEC 2015 Competition on Learning-based Real-Parameter Single Objective Optimization," Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China And Technical Report, Nanyang Technological University, Singapore 2014.

Sürekli Optimizasyon Problemleri için Çoklu Arama Stratejisi Kullanan Fil Sürü Optimizasyonu

Year 2019, Volume: 7 Issue: 2, 261 - 268, 25.05.2019
https://doi.org/10.21541/apjes.455717

Abstract

Fillerin sosyal davranışlarını taklit eden fil sürü optimizasyonu (EHO), yakın zamanda önerilen sürü zekası ve popülasyon tabanlı
bir optimizasyon algoritmasıdır. EHO, yerel arama konusunda iyi bir yeteneğe sahip olmasına rağmen popülasyon çeşitliliğini
erken kaybetmesi nedeniyle global aramada etkili olamamaktadır. Temel EHO yönteminde, yeni bireylerin oluşturulması için
tek bir çözüm arama denklemi kullanılmaktadır. Bu nedenle, arama uzayının etkili bir şekilde araştırılmasında ve farklı
karakteristikteki problemlerin çözümünde yetersiz kalmaktadır. Bu çalışmada, bu sorunların üstesinden gelmek ve keşif ve
faydalanma arasındaki dengeyi sağlayabilmek için en çok bilinen optimizasyon tekniklerinin arama stratejilerinden esinlenilerek
çoklu arama stratejisi kullanan fil sürü optimizasyonu (Multi-EHO) önerilmiştir. Önerilen yöntem ile temel EHO‘nun
karşılaştırılması için farklı karakteristikteki 15 fonksiyona sahip CEC2015 test seti kullanılmıştır. Ayrıca Multi-EHO’nun
performansını doğrulamak için, önerilen yöntem son yıllarda önerilen gri kurt algoritması (GWO) ve balina optimizasyonu
algoritması (WOA) ile karşılaştırılmıştır. Deneysel sonuçlar, önerilen yöntemin diğer yöntemlere göre daha başarılı ve daha
sağlam bir performansa sahip olduğunu göstermektedir.

References

  • [1] H. Hakli and H. Uguz, "A novel particle swarm optimization algorithm with Levy flight," Applied Soft Computing, vol. 23, pp. 333-345, Oct 2014.
  • [2] I. Strumberger, N. Bacanin, and M. Tuba, "Hybridized Elephant Herding Optimization Algorithm for Constrained Optimization," Cham, 2018, pp. 158-166.
  • [3] D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Technical Report-TR06, Erciyes University, Engineering Faculty, Comput. Eng.Dep.2005.
  • [4] J. Kennedy and R. Eberhart, "Particle swarm optimization," presented at the Sixth International Symposium on Micro Machine and Human Science, Nagoya,Japan, 1995.
  • [5] M. Dorigo and G. D. Caro, "Ant colony optimization: a new meta-heuristic," presented at the Proceedings of the 1999 Congress on Evolutionary Computation, Washington,DC., 1999.
  • [6] S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey Wolf Optimizer," Advances in Engineering Software, vol. 69, pp. 46-61, Mar 2014.
  • [7] S. Mirjalili and A. Lewis, "The Whale Optimization Algorithm," Advances in Engineering Software, vol. 95, pp. 51-67, May 2016.
  • [8] G. G. Wang, S. Deb, and L. D. Coelho, "Elephant Herding Optimization," 2015 3rd International Symposium on Computational and Business Intelligence (Iscbi 2015), pp. 1-5, 2015.
  • [9] I. Strumberger, N. Bacanin, S. Tomic, M. Beko, and M. Tuba, "Static Drone Placement by Elephant Herding Optimization Algorithm," 2017 25th Telecommunication Forum (Telfor), pp. 808-811, 2017.
  • [10] E. Tuba, A. Alihodzic, and M. Tuba, "Multilevel Image Thresholding Using Elephant Herding Optimization Algorithm," 2017 14th International Conference on Engineering of Modern Electric Systems (Emes), pp. 240-243, 2017.
  • [11] E. Tuba and Z. Stanimirovic, "Elephant Herding Optimization Algorithm for Support Vector Machine Parameters Tuning," Proceedings of the 9th International Conference on Electronics, Computers and Artificial Intelligence - Ecai 2017, 2017.
  • [12] A. Alihodzic, E. Tuba, R. Capor-Hrosik, E. Dolicanin, and M. Tuba, "Unmanned Aerial Vehicle Path Planning Problem by Adjusted Elephant Herding Optimization," 2017 25th Telecommunication Forum (Telfor), pp. 804-807, 2017.
  • [13] M. A. Sarwar, B. Amin, N. Ayub, S. H. Faraz, S. U. R. Khan, and N. Javaid, "Scheduling of Appliances in Home Energy Management System Using Elephant Herding Optimization and Enhanced Differential Evolution," Advances in Intelligent Networking and Collaborative Systems, Incos-2017, vol. 8, pp. 132-142, 2018.
  • [14] D. K. Sambariya and R. Fagna, "A Robust PID Controller for Load Frequency Control of Single Area Re-heat Thermal Power Plant using Elephant Herding Optimization Techniques," 2017 Ieee International Conference on Information, Communication, Instrumentation and Control (Icicic), 2017.
  • [15] V. Tuba, M. Beko, and M. Tuba, "Performance of Elephant Herding Optimization Algorithm on CEC 2013 real parameter single objective optimization," WSEAS TRANSACTIONS on SYSTEMS, vol. 16, pp. 100-105, 2017.
  • [16] S. Parashar, A. Swarnkar, K. R. Niazi, and N. Gupta, "A modified elephant herding optimization for economic generation co-ordination of DERs and BESS in grid connected microgrid," Journal of Engineering-Joe, Nov 15 2017.
  • [17] N. K. Meena, S. Parashar, A. Swarnkar, N. Gupta, and K. R. Niazi, "Improved Elephant Herding Optimization for Multiobjective DER Accommodation in Distribution Systems," IEEE Transactions on Industrial Informatics, vol. PP, 2017.
  • [18] E. Tuba, R. Capor-Hrosik, A. Alihodzic, R. Jovanovic, and M. Tuba, "Chaotic Elephant Herding Optimization Algorithm," presented at the IEEE 16th World Symposium on Applied Machine Intelligence and Informatics, Kosice,Slovakia, 2018.
  • [19] R. Storn and K. Price, "Differential evolution-A simple and efficient adaptive scheme for global optimization over continuous spaces," Berkeley: ICSI, 1995.
  • [20] G. G. Wang, S. Deb, X. Z. Gao, and L. D. Coelho, "A new metaheuristic optimisation algorithm motivated by elephant herding behaviour," International Journal of Bio-Inspired Computation, vol. 8, pp. 394-409, 2016.
  • [21] H. Wang, Z. J. Wu, S. Rahnamayan, H. Sun, Y. Liu, and J. S. Pan, "Multi-strategy ensemble artificial bee colony algorithm," Information Sciences, vol. 279, pp. 587-603, Sep 20 2014.
  • [22] M. S. Kiran, H. Hakli, M. Gunduz, and H. Uguz, "Artificial bee colony algorithm with variable search strategy for continuous optimization," Information Sciences, vol. 300, pp. 140-157, Apr 10 2015.
  • [23] H. Hakli, "A modified cuckoo search using different search strategies," International Journal of Intelligent Systems and Applications in Engineering, vol. 4 (Special Issue), pp. 190-194, 2016.
  • [24] Y. Wang, B. Li, T. Weise, J. Y. Wang, B. Yuan, and Q. J. Tian, "Self-adaptive learning based particle swarm optimization," Information Sciences, vol. 181, pp. 4515-4538, Oct 15 2011.
  • [25] H. Hakli, "An improved elephant herding optimization by balancing local and global search for continuous optimization," presented at the 15th International Conference on Informatics and Information Technologies, CIIT 2018, Mavrovo, Macedonia, 2018.
  • [26] J. J. Liang, B. Y. Qu, P. N. Suganthan, and Q. Chen, "Problem Definitions and Evaluation Criteria for the CEC 2015 Competition on Learning-based Real-Parameter Single Objective Optimization," Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China And Technical Report, Nanyang Technological University, Singapore 2014.
There are 26 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Hüseyin Haklı 0000-0001-5019-071X

Publication Date May 25, 2019
Submission Date August 29, 2018
Published in Issue Year 2019 Volume: 7 Issue: 2

Cite

IEEE H. Haklı, “Sürekli Optimizasyon Problemleri için Çoklu Arama Stratejisi Kullanan Fil Sürü Optimizasyonu”, APJES, vol. 7, no. 2, pp. 261–268, 2019, doi: 10.21541/apjes.455717.