ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Comparison of Hybrid Elephant Herding Optimization with Different Evolutionary Optimization Algorithms


Affiliations
1 Sathyabama Institute of Science and Technology, India
2 Department of Computer Science Engineering, Marian Engineering College, India
     

   Subscribe/Renew Journal


Many optimization algorithms that imitate the social behaviour of animals and natural biological evolution have been proposed in the recently preceding years. These nature inspired algorithms known as evolutionary algorithms have considerably enhanced the development of the optimization process. In this paper, a hybrid elephant herding opposition algorithm is proposed and a comparative study is conducted to analyse the effectiveness of the proposed algorithm. For the purpose of the comparison, the optimization algorithms that have been taken up for the study are Refined Selfish Herd Optimization (RSHO), Spotted Hyena Optimization (SHO), Chicken Swarm Optimization (CSO) and Particle Swarm Optimization (PSO). Tests on 21 common benchmark functions have been conducted to evaluate the performance of the proposed algorithm. The results from the experiment concluded that the proposed algorithm performs better than the other algorithms.

Keywords

Evolutionary Algorithm, Elephant Herding Optimization, Benchmark Functions.
Subscription Login to verify subscription
User
Notifications
Font Size

  • S. Binitha and S.S. Sathya, “A Survey of Bio Inspired Optimization Algorithms”, International journal of Soft Computing and Engineering, Vol. 2, No. 2, pp. 137-151, 2012.

  • A. Darwish, “Bio-Inspired Computing: Algorithms Review, Deep Analysis, and the Scope of applications”, Future Computing and Informatics, Vol. 3, No. 2, pp. 231-246, 2018.

  • R.S. Parpinelli, H.S. Lopes and A.A. Freitas, “Data Mining with An Ant Colony Optimization Algorithm”, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 4, pp. 321-332, 2002.

  • D. Karaboga, “An Idea based on Honey Bee Swarm for Numerical Optimization”, Technical Report, Department of Computer Engineering, Erciyes University, Vol. 200, pp. 1-10, 2005.

  • E. Emary, H.M. Zawbaa and A.E Hassanien, “Binary Grey Wolf Optimization Approaches for Feature Selection”, Neurocomputing, Vol. 172, pp. 371-381, 2016.

  • X. Song, L. Tang, S. Zhao, X. Zhang, L. Li, J. Huang and W. Cai, “Grey Wolf Optimizer for Parameter Estimation in Surface Waves”, Soil Dynamics and Earthquake Engineering, Vol. 75, pp. 147-157, 2015.

  • S. Mirjalili, “How Effective is the Grey Wolf Optimizer in Training Multi-Layer Perceptrons”, Applied Intelligence, Vol. 43, No. 1, pp. 150-161, 2015.

  • A.H. Gandomi and A.H. Alavi, “Krill Herd: A New Bio-Inspired Optimization Algorithm”, Communications in Nonlinear Science and Numerical Simulation, Vol. 17, No. 12, pp. 4831-4845, 2012.

  • X.S. Yang, “Engineering Optimization: An Introduction with Metaheuristic Applications”, Wiley Press, 2010.

  • J. Kennedy and R. Eberhart, “Particle Swarm Optimization”, Proceedings of IEEE International Conference on Neural Networks, pp. 1942-1948, 1995.

  • S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm”, Advances in Engineering Software, Vol. 95, pp. 51-67, 2016.

  • E. Cuevas, M. Cienfuegos, R. Rojas and A. Padilla, “A Computational Intelligence Optimization Algorithm based on the Behavior of the Social-Spider”, Proceedings of IEEE International Conference on Computational Intelligence Applications in Modelling and Control, pp. 123-146, 2015.

  • M. Yazdani and F. Jolai, “Lion Optimization Algorithm (LOA): A Nature-Inspired Metaheuristic Algorithm”, Journal of Computational Design and Engineering, Vol. 3, No. 1, pp. 24-36, 2016.

  • Mostafa A. Elhosseini, Ragab A. El Sehiemy, Yasser I. Rashwan, and X.Z. Gao, “On the Performance Improvement of Elephant Herding Optimization Algorithm”, Knowledge Based Systems, Vol. 166, pp. 58-70, 2019.

  • Shahryar Rahnamayan, Hamid R. Tizhoosh and Magdy M.A. Salama, “Quasi-Oppositional Differential Evolution”, Proceedings of IEEE International Conference on Evolutionary Computation, pp. 2229-2236, 2007.

  • J. Vesterstrom and R. Thomsen, “A Comparative Study of Differential Evolution, Particle Swarm Optimization and Evolutionary Algorithms on Numerical Benchmark Problems”, Proceedings of IEEE International Conference on Evolutionary Computation, 1980-1987, 2014.

  • Dinghui Wu, Shipeng Xu and Fei Kong, “Convergence Analysis and Improvement of the Chicken Swarm Optimization Algorithm”, IEEE Access, Vol. 4, pp. 9400-9412, 2016.

  • Adiljan Yimit, Koji Iigura and Yoshihiro Hagihara, “Refined Selfish Herd Optimizer for Global Optimization Problems”, Expert System with Applications, Vol. 139, pp. 1-16, 2020.

  • Gaurav Dhiman and Vijay Kumar, “Spotted Hyena Optimizer: A Novel Bio-Inspired Based Metaheuristic Technique for Engineering Applications”, Proceedings of IEEE International Conference on Advances in Engineering Software, pp. 1-23, 2017.

  • Vahid Beiranvand, Warren Hare and Yves Lucet, “Best Practices for Comparing Optimization Algorithms”, Proceedings of IEEE International Conference on Optimization and Engineering, pp. 815-848, 2017.

  • Voratas Kachitvichyanukul, “Comparison of Three Evolutionary Algorithms: GA, PSO, and DE”, Industrial Engineering and Management Systems, Vol. 11, No. 3, pp. 215-223, 2012.

  • Pradeep Jangir, Siddharth A. Parmar, Indrajit N. Trivedi and R.H. Bhesdadiya, “A Novel Hybrid Particle Swarm Optimizer with Multi Verse Optimizer for Global Numerical Optimization and Optimal Reactive Power Dispatch Problem”, Engineering Science and Technology, an International Journal, Vol. 20, No. 2, pp. 570-586, 2017.

  • H.L. Shieh, “Modified Particle Swarm Optimization Algorithm with Simulated Annealing Behavior and its Numerical Verification”, Applied Mathematics and Computation, Vol. 218, No. 8, pp. 4365-4383, 2016.

  • Harish Garg, “A Hybrid PSO-GA Algorithm for Constrained Optimization Problems”, Applied Mathematics and Computation, Vol. 274, pp. 292-305, 2016.

  • Jiquan Wang, Zhiwen Cheng, Okan K. Ersoy, Mingxin Zhang, Kexin Sun and Yusheng Bi, “Improvement and Application of Chicken Swarm Optimization for Constrained Optimization”, IEEE Access, Vol. 7, pp. 58053-58072, 2019.

  • Gaurav Dhiman and Vijay Kumar, “Multi-Objective Spotted Hyena Optimizer: A Multi-Objective Optimization Algorithm for Engineering Problems”, Knowledge-Based Systems, Vol. 150, pp. 175-197, 2018.

  • Ivana Strumberger, Nebojsa Bacanin and Milan Tuba, “Hybridized Elephant Herding Optimization Algorithm for Constrained Optimization”, Proceedings of IEEE International Conference on Hybrid Intelligent Systems, 158-166, 2017.

  • Jiang Li, Lihong Guo, Yan Li and Chang Liu, “Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems”, Mathematics, Vol. 7, No. 5, pp. 395-412, 2019.

  • Alaa A.K. Ismaeel, Islam A. Elshaarawy, Essam H. Houssein, Fatma Helmy Ismail and Aboul Ella Hassanien, “Enhanced Elephant Herding Optimization for Global Optimization”, IEEE Access, Vol. 7, pp. 34738-34752, 2019.

  • Ivana Strumberger, Nebojsa Bacanin, Slavisa Tomic, Marko Beko and Milan Tuba, “Static Drone Placement by Elephant Herding Optimization Algorithm”, Proceedings of IEEE International Conference on Telecommunication, pp. 1-8, 2017.

  • Syed Muhammad Mohsin, Nadeem Javaid, Sajjad Ahmad Madani, Syed Muhammad Abrar Akber, Sohaib Manzoor and Javed Ahmad, “Implementing Elephant Herding Optimization Algorithm with different Operation Time Intervals for Appliance Scheduling in Smart Grid”, Proceedings of IEEE International Conference on Advanced Information Networking and Applications, pp. 1-8, 2018.

  • H.R. Tizhoosh, “Opposition-Based Reinforcement Learning”, Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 10, no. 3, pp. 578-585, 2006.

  • M. Basu, “Quasi-Oppositional Differential Evolution for Optimal Reactive Power Dispatch”, Electrical Power and Energy Systems, Vol. 78, pp. 29-40, 2016

  • Provas Kumar Roy and Dharmadas Mandal, “Oppositional Biogeography-Based Optimisation for Optimal Power Flow”, International Journal on Power and Energy Conversion, Vol. 5, No. 1, pp. 47-69, 2014.

  • Qingzheng Xu, Na Wang, Feng Zou and Jungang Yang, “Exploring the Reasons Behind the Good Performance of Opposition-Based Learning”, IEEE Access, Vol. 7, pp. 7259-7272, 2019.

  • Provas Kumar Roy, Chandan Paul and Sneha Sultana, “Oppositional Teaching Learning Based Optimization Approach for Combined Heat and Power Dispatch”, International Journal of Electrical Power and Energy Systems, Vol. 57, pp. 392-403, 2014.

  • Provas Kumar and Sudipta Bhui, “Multiobjective Quasi-Oppositional Teaching Learning based Optimization for Economic Emission Load Dispatch Problem”, International Journal of Electrical Power and Energy Systems, Vol. 53, pp. 937-948, 2013.

  • Mehmet Ergezer, Dan Simon and Dawei Du, “Oppositional Biogeography-Based Optimization", Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 1-8, 2009.

  • Mohamed Abd Elaziz, Diego Oliva and Shengwu Xiong, “An Improved Opposition-Based Sine Cosine Algorithm for global optimization”, Expert Systems with Applications, Vol. 90, pp. 484-500, 2017.

  • Qingzheng Xu, Lei Wang, Na Wang, Xinhong Hei and Li Zhao, “A Review of Opposition-Based Learning from 2005 to 2012”, Engineering Applications of Artificial Intelligence, Vol. 29, pp. 1-12, 2014.

  • G.G. Wang, S. Deb and L.D.S. Coelho, “Elephant Herding Optimization”, Proceedings of 3rd International Symposium on Computational and Business Intelligence, pp. 1-5, 2015.


Abstract Views: 255

PDF Views: 0




  • Comparison of Hybrid Elephant Herding Optimization with Different Evolutionary Optimization Algorithms

Abstract Views: 255  |  PDF Views: 0

Authors

T. Mathi Murugan
Sathyabama Institute of Science and Technology, India
E. Baburaj
Department of Computer Science Engineering, Marian Engineering College, India

Abstract


Many optimization algorithms that imitate the social behaviour of animals and natural biological evolution have been proposed in the recently preceding years. These nature inspired algorithms known as evolutionary algorithms have considerably enhanced the development of the optimization process. In this paper, a hybrid elephant herding opposition algorithm is proposed and a comparative study is conducted to analyse the effectiveness of the proposed algorithm. For the purpose of the comparison, the optimization algorithms that have been taken up for the study are Refined Selfish Herd Optimization (RSHO), Spotted Hyena Optimization (SHO), Chicken Swarm Optimization (CSO) and Particle Swarm Optimization (PSO). Tests on 21 common benchmark functions have been conducted to evaluate the performance of the proposed algorithm. The results from the experiment concluded that the proposed algorithm performs better than the other algorithms.

Keywords


Evolutionary Algorithm, Elephant Herding Optimization, Benchmark Functions.

References